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Knowledge Mining With Scene Text for Fine-Grained Recognition | Hao Wang, Junchao Liao, Tianheng Cheng, Zewen Gao, Hao Liu, Bo Ren, Xiang Bai, Wenyu Liu | Recently, the semantics of scene text has been proven to be essential in fine-grained image classification. However, the existing methods mainly exploit the literal meaning of scene text for fine-grained recognition, which might be irrelevant when it is not significantly related to objects/scenes. We propose an end-to-end trainable network that mines implicit contextual knowledge behind scene text image and enhance the semantics and correlation to fine-tune the image representation. Unlike the existing methods, our model integrates three modalities: visual feature extraction, text semantics extraction, and correlating background knowledge to fine-grained image classification. Specifically, we employ KnowBert to retrieve relevant knowledge for semantic representation and combine it with image features for fine-grained classification. Experiments on two benchmark datasets, Con-Text, and Drink Bottle, show that our method outperforms the state-of-the-art by 3.72% mAP and 5.39% mAP, respectively. To further validate the effectiveness of the proposed method, we create a new dataset on crowd activity recognition for the evaluation. The source code, new dataset, and pre-trained models of this work will be publicly available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Knowledge_Mining_With_Scene_Text_for_Fine-Grained_Recognition_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.14215 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Knowledge_Mining_With_Scene_Text_for_Fine-Grained_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Knowledge_Mining_With_Scene_Text_for_Fine-Grained_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Supervised Learning of Object Parts for Semantic Segmentation | Adrian Ziegler, Yuki M. Asano | Progress in self-supervised learning has brought strong general image representation learning methods. Yet so far, it has mostly focused on image-level learning. In turn, tasks such as unsupervised image segmentation have not benefited from this trend as they require spatially-diverse representations. However, learning dense representations is challenging, as in the unsupervised context it is not clear how to guide the model to learn representations that correspond to various potential object categories. In this paper, we argue that self-supervised learning of object parts is a solution to this issue. Object parts are generalizable: they are a priori independent of an object definition, but can be grouped to form objects a posteriori. To this end, we leverage the recently proposed Vision Transformer's capability of attending to objects and combine it with a spatially dense clustering task for fine-tuning the spatial tokens. Our method surpasses the state-of-the-art on three semantic segmentation benchmarks by 17%-3%, showing that our representations are versatile under various object definitions. Finally, we extend this to fully unsupervised segmentation - which refrains completely from using label information even at test-time - and demonstrate that a simple method for automatically merging discovered object parts based on community detection yields substantial gains. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ziegler_Self-Supervised_Learning_of_Object_Parts_for_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ziegler_Self-Supervised_Learning_of_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.13101 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ziegler_Self-Supervised_Learning_of_Object_Parts_for_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ziegler_Self-Supervised_Learning_of_Object_Parts_for_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects | Manuel Stoiber, Martin Sundermeyer, Rudolph Triebel | Tracking objects in 3D space and predicting their 6DoF pose is an essential task in computer vision. State-of-the-art approaches often rely on object texture to tackle this problem. However, while they achieve impressive results, many objects do not contain sufficient texture, violating the main underlying assumption. In the following, we thus propose ICG, a novel probabilistic tracker that fuses region and depth information and only requires the object geometry. Our method deploys correspondence lines and points to iteratively refine the pose. We also implement robust occlusion handling to improve performance in real-world settings. Experiments on the YCB-Video, OPT, and Choi datasets demonstrate that, even for textured objects, our approach outperforms the current state of the art with respect to accuracy and robustness. At the same time, ICG shows fast convergence and outstanding efficiency, requiring only 1.3 ms per frame on a single CPU core. Finally, we analyze the influence of individual components and discuss our performance compared to deep learning-based methods. The source code of our tracker is publicly available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Stoiber_Iterative_Corresponding_Geometry_Fusing_Region_and_Depth_for_Highly_Efficient_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Stoiber_Iterative_Corresponding_Geometry_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.05334 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Stoiber_Iterative_Corresponding_Geometry_Fusing_Region_and_Depth_for_Highly_Efficient_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Stoiber_Iterative_Corresponding_Geometry_Fusing_Region_and_Depth_for_Highly_Efficient_CVPR_2022_paper.html | CVPR 2022 | null |
Single-Photon Structured Light | Varun Sundar, Sizhuo Ma, Aswin C. Sankaranarayanan, Mohit Gupta | We present a novel structured light technique that uses Single Photon Avalanche Diode (SPAD) arrays to enable 3D scanning at high-frame rates and low-light levels. This technique, called "Single-Photon Structured Light", works by sensing binary images that indicates the presence or absence of photon arrivals during each exposure; the SPAD array is used in conjunction with a high-speed binary projector, with both devices operated at speeds as high as 20 kHz. The binary images that we acquire are heavily influenced by photon noise and are easily corrupted by ambient sources of light. To address this, we develop novel temporal sequences using error correction codes that are designed to be robust to short-range effects like projector and camera defocus as well as resolution mismatch between the two devices. Our lab prototype is capable of 3D imaging in challenging scenarios involving objects with extremely low albedo or undergoing fast motion, as well as scenes under strong ambient illumination. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sundar_Single-Photon_Structured_Light_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sundar_Single-Photon_Structured_Light_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.05300 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sundar_Single-Photon_Structured_Light_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sundar_Single-Photon_Structured_Light_CVPR_2022_paper.html | CVPR 2022 | null |
Deblurring via Stochastic Refinement | Jay Whang, Mauricio Delbracio, Hossein Talebi, Chitwan Saharia, Alexandros G. Dimakis, Peyman Milanfar | Image deblurring is an ill-posed problem with multiple plausible solutions for a given input image. However, most existing methods produce a deterministic estimate of the clean image and are trained to minimize pixel-level distortion. These metrics are known to be poorly correlated with human perception, and often lead to unrealistic reconstructions. We present an alternative framework for blind deblurring based on conditional diffusion models. Unlike existing techniques, we train a stochastic sampler that refines the output of a deterministic predictor and is capable of producing a diverse set of plausible reconstructions for a given input. This leads to a significant improvement in perceptual quality over existing state-of-the-art methods across multiple standard benchmarks. Our predict-and-refine approach also enables much more efficient sampling compared to typical diffusion models. Combined with a carefully tuned network architecture and inference procedure, our method is competitive in terms of distortion metrics such as PSNR. These results show clear benefits of our diffusion-based method for deblurring and challenge the widely used strategy of producing a single, deterministic reconstruction. | https://openaccess.thecvf.com/content/CVPR2022/papers/Whang_Deblurring_via_Stochastic_Refinement_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Whang_Deblurring_via_Stochastic_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.02475 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Whang_Deblurring_via_Stochastic_Refinement_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Whang_Deblurring_via_Stochastic_Refinement_CVPR_2022_paper.html | CVPR 2022 | null |
3DJCG: A Unified Framework for Joint Dense Captioning and Visual Grounding on 3D Point Clouds | Daigang Cai, Lichen Zhao, Jing Zhang, Lu Sheng, Dong Xu | Observing that the 3D captioning task and the 3D grounding task contain both shared and complementary information in nature, in this work, we propose a unified framework to jointly solve these two distinct but closely related tasks in a synergistic fashion, which consists of both shared task-agnostic modules and lightweight task-specific modules. On one hand, the shared task-agnostic modules aim to learn precise locations of objects, fine-grained attribute features to characterize different objects, and complex relations between objects, which benefit both captioning and visual grounding. On the other hand, by casting each of the two tasks as the proxy task of another one, the lightweight task-specific modules solve the captioning task and the grounding task respectively. Extensive experiments and ablation study on three 3D vision and language datasets demonstrate that our joint training framework achieves significant performance gains for each individual task and finally improves the state-of-the-art performance for both captioning and grounding tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cai_3DJCG_A_Unified_Framework_for_Joint_Dense_Captioning_and_Visual_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cai_3DJCG_A_Unified_Framework_for_Joint_Dense_Captioning_and_Visual_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cai_3DJCG_A_Unified_Framework_for_Joint_Dense_Captioning_and_Visual_CVPR_2022_paper.html | CVPR 2022 | null |
TransGeo: Transformer Is All You Need for Cross-View Image Geo-Localization | Sijie Zhu, Mubarak Shah, Chen Chen | The dominant CNN-based methods for cross-view image geo-localization rely on polar transform and fail to model global correlation. We propose a pure transformer-based approach (TransGeo) to address these limitations from a different perspective. TransGeo takes full advantage of the strengths of transformer related to global information modeling and explicit position information encoding. We further leverage the flexibility of transformer input and propose an attention-guided non-uniform cropping method, so that uninformative image patches are removed with negligible drop on performance to reduce computation cost. The saved computation can be reallocated to increase resolution only for informative patches, resulting in performance improvement with no additional computation cost. This "attend and zoom-in" strategy is highly similar to human behavior when observing images. Remarkably, TransGeo achieves state-of-the-art results on both urban and rural datasets, with significantly less computation cost than CNN-based methods. It does not rely on polar transform and infers faster than CNN-based methods. Code is available at https://github.com/Jeff-Zilence/TransGeo2022. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_TransGeo_Transformer_Is_All_You_Need_for_Cross-View_Image_Geo-Localization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_TransGeo_Transformer_Is_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.00097 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_TransGeo_Transformer_Is_All_You_Need_for_Cross-View_Image_Geo-Localization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_TransGeo_Transformer_Is_All_You_Need_for_Cross-View_Image_Geo-Localization_CVPR_2022_paper.html | CVPR 2022 | null |
R(Det)2: Randomized Decision Routing for Object Detection | Yali Li, Shengjin Wang | In the paradigm of object detection, the decision head is an important part, which affects detection performance significantly. Yet how to design a high-performance decision head remains to be an open issue. In this paper, we propose a novel approach to combine decision trees and deep neural networks in an end-to-end learning manner for object detection. First, we disentangle the decision choices and prediction values by plugging soft decision trees into neural networks. To facilitate the effective learning, we propose the randomized decision routing with node selective and associative losses, which can boost the feature representative learning and network decision simultaneously. Second, we develop the decision head for object detection with narrow branches to generate the routing probabilities and masks, for the purpose of obtaining divergent decisions from different nodes. We name this approach as the randomized decision routing for object detection, abbreviated as R(Det)^2. Experiments on MS-COCO dataset demonstrate that R(Det)^2 is effective to improve the detection performance. Equipped with existing detectors, it achieves 1.4~ 3.6% AP improvement. Code will be released soon. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_RDet2_Randomized_Decision_Routing_for_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_RDet2_Randomized_Decision_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_RDet2_Randomized_Decision_Routing_for_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_RDet2_Randomized_Decision_Routing_for_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Abandoning the Bayer-Filter To See in the Dark | Xingbo Dong, Wanyan Xu, Zhihui Miao, Lan Ma, Chao Zhang, Jiewen Yang, Zhe Jin, Andrew Beng Jin Teoh, Jiajun Shen | Low-light image enhancement, a pervasive but challenging problem, plays a central role in enhancing the visibility of an image captured in a poor illumination environment. Due to the fact that not all photons can pass the Bayer-Filter on the sensor of the color camera, in this work, we first present a De-Bayer-Filter simulator based on deep neural networks to generate a monochrome raw image from the colored raw image. Next, a fully convolutional network is proposed to achieve the low-light image enhancement by fusing colored raw data with synthesized monochrome data. Channel-wise attention is also introduced to the fusion process to establish a complementary interaction between features from colored and monochrome raw images. To train the convolutional networks, we propose a dataset with monochrome and color raw pairs named Mono-Colored Raw paired dataset (MCR) collected by using a monochrome camera without Bayer-Filter and a color camera with Bayer-Filter. The proposed pipeline take advantages of the fusion of the virtual monochrome and the color raw images and our extensive experiments indicate that significant improvement can be achieved by leveraging raw sensor data and data-driven learning. | https://openaccess.thecvf.com/content/CVPR2022/papers/Dong_Abandoning_the_Bayer-Filter_To_See_in_the_Dark_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dong_Abandoning_the_Bayer-Filter_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.04042 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Abandoning_the_Bayer-Filter_To_See_in_the_Dark_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Abandoning_the_Bayer-Filter_To_See_in_the_Dark_CVPR_2022_paper.html | CVPR 2022 | null |
SASIC: Stereo Image Compression With Latent Shifts and Stereo Attention | Matthias Wödlinger, Jan Kotera, Jan Xu, Robert Sablatnig | We propose a learned method for stereo image compression that leverages the similarity of the left and right images in a stereo pair due to overlapping fields of view. The left image is compressed by a learned compression method based on an autoencoder with a hyperprior entropy model. The right image uses this information from the previously encoded left image in both the encoding and decoding stages. In particular, for the right image, we encode only the residual of its latent representation to the optimally shifted latent of the left image. On top of that, we also employ a stereo attention module to connect left and right images during decoding. The performance of the proposed method is evaluated on two benchmark stereo image datasets (Cityscapes and InStereo2K) and outperforms previous stereo image compression methods while being significantly smaller in model size. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wodlinger_SASIC_Stereo_Image_Compression_With_Latent_Shifts_and_Stereo_Attention_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wodlinger_SASIC_Stereo_Image_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wodlinger_SASIC_Stereo_Image_Compression_With_Latent_Shifts_and_Stereo_Attention_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wodlinger_SASIC_Stereo_Image_Compression_With_Latent_Shifts_and_Stereo_Attention_CVPR_2022_paper.html | CVPR 2022 | null |
Exploiting Temporal Relations on Radar Perception for Autonomous Driving | Peizhao Li, Pu Wang, Karl Berntorp, Hongfu Liu | We consider the object recognition problem in autonomous driving using automotive radar sensors. Comparing to Lidar sensors, radar is cost-effective and robust in all-weather conditions for perception in autonomous driving. However, radar signals suffer from low angular resolution and precision in recognizing surrounding objects. To enhance the capacity of automotive radar, in this work, we exploit the temporal information from successive ego-centric bird-eye-view radar image frames for radar object recognition. We leverage the consistency of an object's existence and attributes (size, orientation, etc.), and propose a temporal relational layer to explicitly model the relations between objects within successive radar images. In both object detection and multiple object tracking, we show the superiority of our method compared to several baseline approaches. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Exploiting_Temporal_Relations_on_Radar_Perception_for_Autonomous_Driving_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Exploiting_Temporal_Relations_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.01184 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Exploiting_Temporal_Relations_on_Radar_Perception_for_Autonomous_Driving_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Exploiting_Temporal_Relations_on_Radar_Perception_for_Autonomous_Driving_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-Instance Point Cloud Registration by Efficient Correspondence Clustering | Weixuan Tang, Danping Zou | We address the problem of estimating the poses of multiple instances of the source point cloud within a target point cloud. Existing solutions require sampling a lot of hypotheses to detect possible instances and reject the outliers, whose robustness and efficiency degrade notably when the number of instances and outliers increase. We propose to directly group the set of noisy correspondences into different clusters based on a distance invariance matrix. The instances and outliers are automatically identified through clustering. Our method is robust and fast. We evaluated our method on both synthetic and real-world datasets. The results show that our approach can correctly register up to 20 instances with an F1 score of 90.46% in the presence of 70% outliers, which performs significantly better and at least 10x faster than existing methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_Multi-Instance_Point_Cloud_Registration_by_Efficient_Correspondence_Clustering_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tang_Multi-Instance_Point_Cloud_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2111.14582 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Multi-Instance_Point_Cloud_Registration_by_Efficient_Correspondence_Clustering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Multi-Instance_Point_Cloud_Registration_by_Efficient_Correspondence_Clustering_CVPR_2022_paper.html | CVPR 2022 | null |
Contrastive Boundary Learning for Point Cloud Segmentation | Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao | Point cloud segmentation is fundamental in understanding 3D environments. However, current 3D point cloud segmentation methods usually perform poorly on scene boundaries, which degenerates the overall segmentation performance. In this paper, we focus on the segmentation of scene boundaries. Accordingly, we first explore metrics to evaluate the segmentation performance on scene boundaries. To address the unsatisfactory performance on boundaries, we then propose a novel contrastive boundary learning (CBL) framework for point cloud segmentation. Specifically, the proposed CBL enhances feature discrimination between points across boundaries by contrasting their representations with the assistance of scene contexts at multiple scales. By applying CBL on three different baseline methods, we experimentally show that CBL consistently improves different baselines and assists them to achieve compelling performance on boundaries, as well as the overall performance, e.g. in mIoU. The experimental results demonstrate the effectiveness of our method and the importance of boundaries for 3D point cloud segmentation. Code and model will be made publicly available at https://github.com/LiyaoTang/contrastBoundary. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_Contrastive_Boundary_Learning_for_Point_Cloud_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tang_Contrastive_Boundary_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.05272 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Contrastive_Boundary_Learning_for_Point_Cloud_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Contrastive_Boundary_Learning_for_Point_Cloud_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution | Jie Liang, Hui Zeng, Lei Zhang | Single image super-resolution (SISR) with generative adversarial networks (GAN) has recently attracted increasing attention due to its potentials to generate rich details. However, the training of GAN is unstable, and it often introduces many perceptually unpleasant artifacts along with the generated details. In this paper, we demonstrate that it is possible to train a GAN-based SISR model which can stably generate perceptually realistic details while inhibiting visual artifacts. Based on the observation that the local statistics (e.g., residual variance) of artifact areas are often different from the areas of perceptually friendly details, we develop a framework to discriminate between GAN-generated artifacts and realistic details, and consequently generate an artifact map to regularize and stabilize the model training process. Our proposed locally discriminative learning (LDL) method is simple yet effective, which can be easily plugged in off-the-shelf SISR methods and boost their performance. Experiments demonstrate that LDL outperforms the state-of-the-art GAN based SISR methods, achieving not only higher reconstruction accuracy but also superior perceptual quality on both synthetic and real-world datasets. Codes and models are available at https://github.com/csjliang/LDL. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liang_Details_or_Artifacts_A_Locally_Discriminative_Learning_Approach_to_Realistic_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liang_Details_or_Artifacts_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09195 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liang_Details_or_Artifacts_A_Locally_Discriminative_Learning_Approach_to_Realistic_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liang_Details_or_Artifacts_A_Locally_Discriminative_Learning_Approach_to_Realistic_CVPR_2022_paper.html | CVPR 2022 | null |
CVNet: Contour Vibration Network for Building Extraction | Ziqiang Xu, Chunyan Xu, Zhen Cui, Xiangwei Zheng, Jian Yang | The classic active contour model raises a great promising solution to polygon-based object extraction with the progress of deep learning recently. Inspired by the physical vibration theory, we propose a contour vibration network (CVNet) for automatic building boundary delineation. Different from the previous contour models, the CVNet originally roots in the force and motion principle of contour string. Through the infinitesimal analysis and Newton's second law, we derive the spatial-temporal contour vibration model of object shapes, which is mathematically reduced to second-order differential equation. To concretize the dynamic model, we transform the vibration model into the space of image features, and reparameterize the equation coefficients as the learnable state from feature domain. The contour changes are finally evolved in a progressive mode through the computation of contour vibration equation. Both the polygon contour evolution and the model optimization are modulated to form a close-looping end-to-end network. Comprehensive experiments on three datasets demonstrate the effectiveness and superiority of our CVNet over other baselines and state-of-the-art methods for the polygon-based building extraction. The code is available at https://github.com/xzq-njust/CVNet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_CVNet_Contour_Vibration_Network_for_Building_Extraction_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_CVNet_Contour_Vibration_Network_for_Building_Extraction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_CVNet_Contour_Vibration_Network_for_Building_Extraction_CVPR_2022_paper.html | CVPR 2022 | null |
Hyperbolic Image Segmentation | Mina Ghadimi Atigh, Julian Schoep, Erman Acar, Nanne van Noord, Pascal Mettes | For image segmentation, the current standard is to perform pixel-level optimization and inference in Euclidean output embedding spaces through linear hyperplanes. In this work, we show that hyperbolic manifolds provide a valuable alternative for image segmentation and propose a tractable formulation of hierarchical pixel-level classification in hyperbolic space. Hyperbolic Image Segmentation opens up new possibilities and practical benefits for segmentation, such as uncertainty estimation and boundary information for free, zero-label generalization, and increased performance in low-dimensional output embeddings. | https://openaccess.thecvf.com/content/CVPR2022/papers/Atigh_Hyperbolic_Image_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Atigh_Hyperbolic_Image_Segmentation_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Atigh_Hyperbolic_Image_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Atigh_Hyperbolic_Image_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Forward Compatible Training for Large-Scale Embedding Retrieval Systems | Vivek Ramanujan, Pavan Kumar Anasosalu Vasu, Ali Farhadi, Oncel Tuzel, Hadi Pouransari | In visual retrieval systems, updating the embedding model requires recomputing features for every piece of data. This expensive process is referred to as backfilling. Recently, the idea of backward compatible training (BCT) was proposed. To avoid the cost of backfilling, BCT modifies training of the new model to make its representations compatible with those of the old model. However, BCT can significantly hinder the performance of the new model. In this work, we propose a new learning paradigm for representation learning: forward compatible training (FCT). In FCT, when the old model is trained, we also prepare for a future unknown version of the model. We propose learning side-information, an auxiliary feature for each sample which facilitates future updates of the model. To develop a powerful and flexible framework for model compatibility, we combine side-information with a forward transformation from old to new embeddings. Training of the new model is not modified, hence, its accuracy is not degraded. We demonstrate significant retrieval accuracy improvement compared to BCT for various datasets: ImageNet-1k (+18.1%), Places-365 (+5.4%), and VGG-Face2 (+8.3%). FCT obtains model compatibility when the new and old models are trained across different datasets, losses, and architectures. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ramanujan_Forward_Compatible_Training_for_Large-Scale_Embedding_Retrieval_Systems_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ramanujan_Forward_Compatible_Training_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.02805 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ramanujan_Forward_Compatible_Training_for_Large-Scale_Embedding_Retrieval_Systems_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ramanujan_Forward_Compatible_Training_for_Large-Scale_Embedding_Retrieval_Systems_CVPR_2022_paper.html | CVPR 2022 | null |
Everything at Once - Multi-Modal Fusion Transformer for Video Retrieval | Nina Shvetsova, Brian Chen, Andrew Rouditchenko, Samuel Thomas, Brian Kingsbury, Rogerio S. Feris, David Harwath, James Glass, Hilde Kuehne | Multi-modal learning from video data has seen increased attention recently as it allows training of semantically meaningful embeddings without human annotation, enabling tasks like zero-shot retrieval and action localization. In this work, we present a multi-modal, modality agnostic fusion transformer that learns to exchange information between multiple modalities, such as video, audio, and text, and integrate them into a fused representation in a joined multi-modal embedding space. We propose to train the system with a combinatorial loss on everything at once - any combination of input modalities, such as single modalities as well as pairs of modalities, explicitly leaving out any add-ons such as position or modality encoding. At test time, the resulting model can process and fuse any number of input modalities. Moreover, the implicit properties of the transformer allow to process inputs of different lengths. To evaluate the proposed approach, we train the model on the large scale HowTo100M dataset and evaluate the resulting embedding space on four challenging benchmark datasets obtaining state-of-the-art results in zero-shot video retrieval and zero-shot video action localization. Our code for this work is also available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shvetsova_Everything_at_Once_-_Multi-Modal_Fusion_Transformer_for_Video_Retrieval_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shvetsova_Everything_at_Once_CVPR_2022_supplemental.pdf | https://arxiv.org/abs/2112.04446 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shvetsova_Everything_at_Once_-_Multi-Modal_Fusion_Transformer_for_Video_Retrieval_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shvetsova_Everything_at_Once_-_Multi-Modal_Fusion_Transformer_for_Video_Retrieval_CVPR_2022_paper.html | CVPR 2022 | https://openaccess.thecvf.com |
Swin Transformer V2: Scaling Up Capacity and Resolution | Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo | We present techniques for scaling Swin Transformer [??] up to 3 billion parameters and making it capable of training with images of up to 1,536x1,536 resolution. By scaling up capacity and resolution, Swin Transformer sets new records on four representative vision benchmarks: 84.0% top-1 accuracy on ImageNet-V2 image classification, 63.1 / 54.4 box / mask mAP on COCO object detection, 59.9 mIoU on ADE20K semantic segmentation, and 86.8% top-1 accuracy on Kinetics-400 video action classification. We tackle issues of training instability, and study how to effectively transfer models pre-trained at low resolutions to higher resolution ones. To this aim, several novel technologies are proposed: 1) a residual post normalization technique and a scaled cosine attention approach to improve the stability of large vision models; 2) a log-spaced continuous position bias technique to effectively transfer models pre-trained at low-resolution images and windows to their higher-resolution counterparts. In addition, we share our crucial implementation details that lead to significant savings of GPU memory consumption and thus make it feasible to train large vision models with regular GPUs. Using these techniques and self-supervised pre-training, we successfully train a strong 3 billion Swin Transformer model and effectively transfer it to various vision tasks involving high-resolution images or windows, achieving the state-of-the-art accuracy on a variety of benchmarks. Code is available at https://github.com/microsoft/Swin-Transformer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Swin_Transformer_V2_Scaling_Up_Capacity_and_Resolution_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Swin_Transformer_V2_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.09883 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Swin_Transformer_V2_Scaling_Up_Capacity_and_Resolution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Swin_Transformer_V2_Scaling_Up_Capacity_and_Resolution_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Template: Topology-Aware Reconstruction and Disentangled Generation of 3D Meshes | Ka-Hei Hui, Ruihui Li, Jingyu Hu, Chi-Wing Fu | This paper introduces a novel framework called DT-Net for 3D mesh reconstruction and generation via Disentangled Topology. Beyond previous works, we learn a topology-aware neural template specific to each input then deform the template to reconstruct a detailed mesh while preserving the learned topology. One key insight is to decouple the complex mesh reconstruction into two sub-tasks: topology formulation and shape deformation. Thanks to the decoupling, DT-Net implicitly learns a disentangled representation for the topology and shape in the latent space. Hence, it can enable novel disentangled controls for supporting various shape generation applications, eg, remix the topologies of 3D objects, that are not achievable by previous reconstruction works. Extensive experimental results demonstrate that our method is able to produce high-quality meshes, particularly with diverse topologies, as compared with the state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hui_Neural_Template_Topology-Aware_Reconstruction_and_Disentangled_Generation_of_3D_Meshes_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hui_Neural_Template_Topology-Aware_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hui_Neural_Template_Topology-Aware_Reconstruction_and_Disentangled_Generation_of_3D_Meshes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hui_Neural_Template_Topology-Aware_Reconstruction_and_Disentangled_Generation_of_3D_Meshes_CVPR_2022_paper.html | CVPR 2022 | null |
DEFEAT: Deep Hidden Feature Backdoor Attacks by Imperceptible Perturbation and Latent Representation Constraints | Zhendong Zhao, Xiaojun Chen, Yuexin Xuan, Ye Dong, Dakui Wang, Kaitai Liang | Backdoor attack is a type of serious security threat to deep learning models.An adversary can provide users with a model trained on poisoned data to manipulate prediction behavior in test stage using a backdoor. The backdoored models behave normally on clean images, yet can be activated and output incorrect prediction if the input is stamped with a specific trigger pattern.Most existing backdoor attacks focus on manually defining imperceptible triggers in input space without considering the abnormality of triggers' latent representations in the poisoned model.These attacks are susceptible to backdoor detection algorithms and even visual inspection.In this paper, We propose a novel and stealthy backdoor attack - DEFEAT. It poisons the clean data using adaptive imperceptible perturbation and restricts latent representation during training process to strengthen our attack's stealthiness and resistance to defense algorithms.We conduct extensive experiments on multiple image classifiers using real-world datasets to demonstrate that our attack can 1) hold against the state-of-the-art defenses, 2) deceive the victim model with high attack success without jeopardizing model utility, and 3) provide practical stealthiness on image data. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_DEFEAT_Deep_Hidden_Feature_Backdoor_Attacks_by_Imperceptible_Perturbation_and_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_DEFEAT_Deep_Hidden_Feature_Backdoor_Attacks_by_Imperceptible_Perturbation_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_DEFEAT_Deep_Hidden_Feature_Backdoor_Attacks_by_Imperceptible_Perturbation_and_CVPR_2022_paper.html | CVPR 2022 | null |
Projective Manifold Gradient Layer for Deep Rotation Regression | Jiayi Chen, Yingda Yin, Tolga Birdal, Baoquan Chen, Leonidas J. Guibas, He Wang | Regressing rotations on SO(3) manifold using deep neural networks is an important yet unsolved problem. The gap between the Euclidean network output space and the non-Euclidean SO(3) manifold imposes a severe challenge for neural network learning in both forward and backward passes. While several works have proposed different regression-friendly rotation representations, very few works have been devoted to improving the gradient backpropagating in the backward pass. In this paper, we propose a manifold-aware gradient that directly backpropagates into deep network weights. Leveraging Riemannian optimization to construct a novel projective gradient, our proposed regularized projective manifold gradient (RPMG) method helps networks achieve new state-of-the-art performance in a variety of rotation estimation tasks. Our proposed gradient layer can also be applied to other smooth manifolds such as the unit sphere. Our project page is at https://jychen18.github.io/RPMG. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Projective_Manifold_Gradient_Layer_for_Deep_Rotation_Regression_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Projective_Manifold_Gradient_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2110.11657 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Projective_Manifold_Gradient_Layer_for_Deep_Rotation_Regression_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Projective_Manifold_Gradient_Layer_for_Deep_Rotation_Regression_CVPR_2022_paper.html | CVPR 2022 | null |
CLIMS: Cross Language Image Matching for Weakly Supervised Semantic Segmentation | Jinheng Xie, Xianxu Hou, Kai Ye, Linlin Shen | It has been widely known that CAM (Class Activation Map) usually only activates discriminative object regions and falsely includes lots of object-related backgrounds. As only a fixed set of image-level object labels are available to the WSSS (weakly supervised semantic segmentation) model, it could be very difficult to suppress those diverse background regions consisting of open set objects. In this paper, we propose a novel Cross Language Image Matching (CLIMS) framework, based on the recently introduced Contrastive Language-Image Pre-training (CLIP) model, for WSSS. The core idea of our framework is to introduce natural language supervision to activate more complete object regions and suppress closely-related open background regions. In particular, we design object, background region and text label matching losses to guide the model to excite more reasonable object regions for CAM of each category. In addition, we design a co-occurring background suppression loss to prevent the model from activating closely-related background regions, with a predefined set of class-related background text descriptions. These designs enable the proposed CLIMS to generate a more complete and compact activation map for the target objects. Extensive experiments on PASCAL VOC2012 dataset show that our CLIMS significantly outperforms the previous state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xie_CLIMS_Cross_Language_Image_Matching_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xie_CLIMS_Cross_Language_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_CLIMS_Cross_Language_Image_Matching_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_CLIMS_Cross_Language_Image_Matching_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Learning To Refactor Action and Co-Occurrence Features for Temporal Action Localization | Kun Xia, Le Wang, Sanping Zhou, Nanning Zheng, Wei Tang | The main challenge of Temporal Action Localization is to retrieve subtle human actions from various co-occurring ingredients, e.g., context and background, in an untrimmed video. While prior approaches have achieved substantial progress through devising advanced action detectors, they still suffer from these co-occurring ingredients which often dominate the actual action content in videos. In this paper, we explore two orthogonal but complementary aspects of a video snippet, i.e., the action features and the co-occurrence features. Especially, we develop a novel auxiliary task by decoupling these two types of features within a video snippet and recombining them to generate a new feature representation with more salient action information for accurate action localization. We term our method RefactorNet, which first explicitly factorizes the action content and regularizes its co-occurrence features, and then synthesizes a new action-dominated video representation. Extensive experimental results and ablation studies on THUMOS14 and ActivityNet v1.3 demonstrate that our new representation, combined with a simple action detector, can significantly improve the action localization performance. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xia_Learning_To_Refactor_Action_and_Co-Occurrence_Features_for_Temporal_Action_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xia_Learning_To_Refactor_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xia_Learning_To_Refactor_Action_and_Co-Occurrence_Features_for_Temporal_Action_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xia_Learning_To_Refactor_Action_and_Co-Occurrence_Features_for_Temporal_Action_CVPR_2022_paper.html | CVPR 2022 | null |
It's Time for Artistic Correspondence in Music and Video | Dídac Surís, Carl Vondrick, Bryan Russell, Justin Salamon | We present an approach for recommending a music track for a given video, and vice versa, based on both their temporal alignment and their correspondence at an artistic level. We propose a self-supervised approach that learns this correspondence directly from data, without any need of human annotations. In order to capture the high-level concepts that are required to solve the task, we propose modeling the long-term temporal context of both the video and the music signals, using Transformer networks for each modality. Experiments show that this approach strongly outperforms alternatives that do not exploit the temporal context. The combination of our contributions improve retrieval accuracy up to 10x over prior state of the art. This strong improvement allows us to introduce a wide range of analyses and applications. For instance, we can condition music retrieval based on visually-defined attributes. | https://openaccess.thecvf.com/content/CVPR2022/papers/Suris_Its_Time_for_Artistic_Correspondence_in_Music_and_Video_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Suris_Its_Time_for_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Suris_Its_Time_for_Artistic_Correspondence_in_Music_and_Video_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Suris_Its_Time_for_Artistic_Correspondence_in_Music_and_Video_CVPR_2022_paper.html | CVPR 2022 | null |
Mixed Differential Privacy in Computer Vision | Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto | We introduce AdaMix, an adaptive differentially private algorithm for training deep neural network classifiers using both private and public image data. While pre-training language models on large public datasets has enabled strong differential privacy (DP) guarantees with minor loss of accuracy, a similar practice yields punishing trade-offs in vision tasks. A few-shot or even zero-shot learning baseline that ignores private data can outperform fine-tuning on a large private dataset. AdaMix incorporates few-shot training, or cross-modal zero-shot learning, on public data prior to private fine-tuning, to improve the trade-off. AdaMix reduces the error increase from the non-private upper bound from the 167-311% of the baseline, on average across 6 datasets, to 68-92% depending on the desired privacy level selected by the user. AdaMix tackles the trade-off arising in visual classification, whereby the most privacy sensitive data, corresponding to isolated points in representation space, are also critical for high classification accuracy. In addition, AdaMix comes with strong theoretical privacy guarantees and convergence analysis. | https://openaccess.thecvf.com/content/CVPR2022/papers/Golatkar_Mixed_Differential_Privacy_in_Computer_Vision_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Golatkar_Mixed_Differential_Privacy_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.11481 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Golatkar_Mixed_Differential_Privacy_in_Computer_Vision_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Golatkar_Mixed_Differential_Privacy_in_Computer_Vision_CVPR_2022_paper.html | CVPR 2022 | null |
AdaFace: Quality Adaptive Margin for Face Recognition | Minchul Kim, Anil K. Jain, Xiaoming Liu | Recognition in low quality face datasets is challenging because facial attributes are obscured and degraded. Advances in margin-based loss functions have resulted in enhanced discriminability of faces in the embedding space. Further, previous studies have studied the effect of adaptive losses to assign more importance to misclassified (hard) examples. In this work, we introduce another aspect of adaptiveness in the loss function, namely the image quality. We argue that the strategy to emphasize misclassified samples should be adjusted according to their image quality. Specifically, the relative importance of easy or hard samples should be based on the sample's image quality. We propose a new loss function that emphasizes samples of different difficulties based on their image quality. Our method achieves this in the form of an adaptive margin function by approximating the image quality with feature norms. Extensive experiments show that our method, AdaFace, improves the face recognition performance over the state-of-the-art (SoTA) on four datasets (IJB-B, IJB-C, IJB-S and TinyFace). Code and models are released in Supp. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_AdaFace_Quality_Adaptive_Margin_for_Face_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_AdaFace_Quality_Adaptive_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.00964 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_AdaFace_Quality_Adaptive_Margin_for_Face_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_AdaFace_Quality_Adaptive_Margin_for_Face_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Soft Estimator of Keypoint Scale and Orientation With Probabilistic Covariant Loss | Pei Yan, Yihua Tan, Shengzhou Xiong, Yuan Tai, Yansheng Li | Estimating keypoint scale and orientation is crucial to extracting invariant features under significant geometric changes. Recently, the estimators based on self-supervised learning have been designed to adapt to complex imaging conditions. Such learning-based estimators generally predict a single scalar for the keypoint scale or orientation, called hard estimators. However, hard estimators are difficult to handle the local patches containing structures of different objects or multiple edges. In this paper, a Soft Self-Supervised Estimator (S3Esti) is proposed to overcome this problem by learning to predict multiple scales and orientations. S3Esti involves three core factors. First, the estimator is constructed to predict the discrete distributions of scales and orientations. The elements with high confidence will be kept as the final scales and orientations. Second, a probabilistic covariant loss is proposed to improve the consistency of the scale and orientation distributions under different transformations. Third, an optimization algorithm is designed to minimize the loss function, whose convergence is proved in theory. When combined with different keypoint extraction models, S3Esti generally improves over 50% accuracy in image matching tasks under significant viewpoint changes. In the 3D reconstruction task, S3Esti decreases more than 10% reprojection error and improves the number of registered images. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yan_Learning_Soft_Estimator_of_Keypoint_Scale_and_Orientation_With_Probabilistic_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yan_Learning_Soft_Estimator_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yan_Learning_Soft_Estimator_of_Keypoint_Scale_and_Orientation_With_Probabilistic_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yan_Learning_Soft_Estimator_of_Keypoint_Scale_and_Orientation_With_Probabilistic_CVPR_2022_paper.html | CVPR 2022 | null |
DN-DETR: Accelerate DETR Training by Introducing Query DeNoising | Feng Li, Hao Zhang, Shilong Liu, Jian Guo, Lionel M. Ni, Lei Zhang | We present in this paper a novel denoising training method to speedup DETR (DEtection TRansformer) training and offer a deepened understanding of the slow convergence issue of DETR-like methods. We show that the slow convergence results from the instability of bipartite graph matching which causes inconsistent optimization goals in early training stages. To address this issue, except for the Hungarian loss, our method additionally feeds ground-truth bounding boxes with noises into Transformer decoder and trains the model to reconstruct the original boxes, which effectively reduces the bipartite graph matching difficulty and leads to a faster convergence. Our method is universal and can be easily plugged into any DETR-like methods by adding dozens of lines of code to achieve a remarkable improvement. As a result, our DN-DETR results in a remarkable improvement (+1.9AP) under the same setting and achieves the best result (AP 43.4 and 48.6 with 12 and 50 epochs of training respectively) among DETR-like methods with ResNet-50 backbone. Our code will be released after the blind review. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_DN-DETR_Accelerate_DETR_Training_by_Introducing_Query_DeNoising_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_DN-DETR_Accelerate_DETR_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_DN-DETR_Accelerate_DETR_Training_by_Introducing_Query_DeNoising_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_DN-DETR_Accelerate_DETR_Training_by_Introducing_Query_DeNoising_CVPR_2022_paper.html | CVPR 2022 | null |
HCSC: Hierarchical Contrastive Selective Coding | Yuanfan Guo, Minghao Xu, Jiawen Li, Bingbing Ni, Xuanyu Zhu, Zhenbang Sun, Yi Xu | Hierarchical semantic structures naturally exist in an image dataset, in which several semantically relevant image clusters can be further integrated into a larger cluster with coarser-grained semantics. Capturing such structures with image representations can greatly benefit the semantic understanding on various downstream tasks. Existing contrastive representation learning methods lack such an important model capability. In addition, the negative pairs used in these methods are not guaranteed to be semantically distinct, which could further hamper the structural correctness of learned image representations. To tackle these limitations, we propose a novel contrastive learning framework called Hierarchical Contrastive Selective Coding (HCSC). In this framework, a set of hierarchical prototypes are constructed and also dynamically updated to represent the hierarchical semantic structures underlying the data in the latent space. To make image representations better fit such semantic structures, we employ and further improve conventional instance-wise and prototypical contrastive learning via an elaborate pair selection scheme. This scheme seeks to select more diverse positive pairs with similar semantics and more precise negative pairs with truly distinct semantics. On extensive downstream tasks, we verify the superior performance of HCSC over state-of-the-art contrastive methods, and the effectiveness of major model components is proved by plentiful analytical studies. We are continually building a comprehensive model zoo (see supplementary material). Our source code and model weights are available at https://github.com/gyfastas/HCSC. | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_HCSC_Hierarchical_Contrastive_Selective_Coding_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_HCSC_Hierarchical_Contrastive_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.00455 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_HCSC_Hierarchical_Contrastive_Selective_Coding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_HCSC_Hierarchical_Contrastive_Selective_Coding_CVPR_2022_paper.html | CVPR 2022 | null |
TransRank: Self-Supervised Video Representation Learning via Ranking-Based Transformation Recognition | Haodong Duan, Nanxuan Zhao, Kai Chen, Dahua Lin | Recognizing transformation types applied to a video clip (RecogTrans) is a long-established paradigm for self-supervised video representation learning, which achieves much inferior performance compared to instance discrimination approaches (InstDisc) in recent works. However, based on a thorough comparison of representative RecogTrans and InstDisc methods, we observe the great potential of RecogTrans on both semantic-related and temporal-related downstream tasks. Based on hard-label classification, existing RecogTrans approaches suffer from noisy supervision signals in pre-training. To mitigate this problem, we developed TransRank, a unified framework for recognizing Transformations in a Ranking formulation. TransRank provides accurate supervision signals by recognizing transformations relatively, consistently outperforming the classification-based formulation. Meanwhile, the unified framework can be instantiated with an arbitrary set of temporal or spatial transformations, demonstrating good generality. With a ranking-based formulation and several empirical practices, we achieve competitive performance on video retrieval and action recognition.Under the same setting, TransRank surpasses the previous state-of-the-art method by 6.4% on UCF101 and 8.3% on HMDB51 for action recognition (Top1 Acc); improves video retrieval on UCF101 by 20.4% (R@1). The promising results validate that RecogTrans is still a worth exploring paradigm for video self-supervised learning. Codes will be released at https://github.com/kennymckormick/TransRank. | https://openaccess.thecvf.com/content/CVPR2022/papers/Duan_TransRank_Self-Supervised_Video_Representation_Learning_via_Ranking-Based_Transformation_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Duan_TransRank_Self-Supervised_Video_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.02028 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Duan_TransRank_Self-Supervised_Video_Representation_Learning_via_Ranking-Based_Transformation_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Duan_TransRank_Self-Supervised_Video_Representation_Learning_via_Ranking-Based_Transformation_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
KeyTr: Keypoint Transporter for 3D Reconstruction of Deformable Objects in Videos | David Novotny, Ignacio Rocco, Samarth Sinha, Alexandre Carlier, Gael Kerchenbaum, Roman Shapovalov, Nikita Smetanin, Natalia Neverova, Benjamin Graham, Andrea Vedaldi | We consider the problem of reconstructing the depth of dynamic objects from videos. Recent progress in dynamic video depth prediction has focused on improving the output of monocular depth estimators by means of multi-view constraints while imposing little to no restrictions on the deformation of the dynamic parts of the scene. However, the theory of Non-Rigid Structure from Motion prescribes to constrain the deformations for 3D reconstruction. We thus propose a new model that departs significantly from this prior work. The idea is to fit a dynamic point cloud to the video data using Sinkhorn's algorithm to associate the 3D points to 2D pixels and use a differentiable point renderer to ensure the compatibility of the 3D deformations with the measured optical flow. In this manner, our algorithm, called Keypoint Transporter, models the overall deformation of the object within the entire video, so it can constrain the reconstruction correspondingly. Compared to weaker deformation models, this significantly reduces the reconstruction ambiguity and, for dynamic objects, allows Keypoint Transporter to obtain reconstructions of the quality superior or at least comparable to prior approaches while being much faster and reliant on a pre-trained monocular depth estimator network. To assess the method, we evaluate on new datasets of synthetic videos depicting dynamic humans and animals with ground-truth depth. We also show qualitative results on crowd-sourced real-world videos of pets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Novotny_KeyTr_Keypoint_Transporter_for_3D_Reconstruction_of_Deformable_Objects_in_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Novotny_KeyTr_Keypoint_Transporter_for_3D_Reconstruction_of_Deformable_Objects_in_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Novotny_KeyTr_Keypoint_Transporter_for_3D_Reconstruction_of_Deformable_Objects_in_CVPR_2022_paper.html | CVPR 2022 | null |
Invariant Grounding for Video Question Answering | Yicong Li, Xiang Wang, Junbin Xiao, Wei Ji, Tat-Seng Chua | Video Question Answering (VideoQA) is the task of answering questions about a video. At its core is understanding the alignments between visual scenes in video and linguistic semantics in question to yield the answer. In leading VideoQA models, the typical learning objective, empirical risk minimization (ERM), latches on superficial correlations between video-question pairs and answers as the alignments. However, ERM can be problematic, because it tends to over-exploit the spurious correlations between question-irrelevant scenes and answers, instead of inspecting the causal effect of question-critical scenes. As a result, the VideoQA models suffer from unreliable reasoning. In this work, we first take a causal look at VideoQA and argue that invariant grounding is the key to ruling out the spurious correlations. Towards this end, we propose a new learning framework, Invariant Grounding for VideoQA (IGV), to ground the question-critical scene, whose causal relations with answers are invariant across different interventions on the complement. With IGV, the VideoQA models are forced to shield the answering process from the negative influence of spurious correlations, which significantly improves the reasoning ability. Experiments on three benchmark datasets validate the superiority of IGV in terms of accuracy, visual explainability, and generalization ability over the leading baselines. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Invariant_Grounding_for_Video_Question_Answering_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Invariant_Grounding_for_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Invariant_Grounding_for_Video_Question_Answering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Invariant_Grounding_for_Video_Question_Answering_CVPR_2022_paper.html | CVPR 2022 | null |
Prompt Distribution Learning | Yuning Lu, Jianzhuang Liu, Yonggang Zhang, Yajing Liu, Xinmei Tian | We present prompt distribution learning for effectively adapting a pre-trained vision-language model to address downstream recognition tasks. Our method not only learns low-bias prompts from a few samples but also captures the distribution of diverse prompts to handle the varying visual representations. In this way, we provide high-quality task-related content for facilitating recognition. This prompt distribution learning is realized by an efficient approach that learns the output embeddings of prompts instead of the input embeddings. Thus, we can employ a Gaussian distribution to model them effectively and derive a surrogate loss for efficient training. Extensive experiments on 12 datasets demonstrate that our method consistently and significantly outperforms existing methods. For example, with 1 sample per category, it relatively improves the average result by 9.1% compared to human-crafted prompts. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lu_Prompt_Distribution_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lu_Prompt_Distribution_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.03340 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lu_Prompt_Distribution_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lu_Prompt_Distribution_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
RAGO: Recurrent Graph Optimizer for Multiple Rotation Averaging | Heng Li, Zhaopeng Cui, Shuaicheng Liu, Ping Tan | This paper proposes a deep recurrent Rotation Averaging Graph Optimizer (RAGO) for Multiple Rotation Averaging (MRA). Conventional optimization-based methods usually fail to produce accurate results due to corrupted and noisy relative measurements. Recent learning-based approaches regard MRA as a regression problem, while these methods are sensitive to initialization due to the gauge freedom problem. To handle these problems, we propose a learnable iterative graph optimizer minimizing a gauge-invariant cost function with an edge rectification strategy to mitigate the effect of inaccurate measurements. Our graph optimizer iteratively refines the global camera rotations by minimizing each node's single rotation objective function. Besides, our approach iteratively rectifies relative rotations to make them more consistent with the current camera orientations and observed relative rotations. Furthermore, we employ a gated recurrent unit to improve the result by tracing the temporal information of the cost graph. Our framework is a real-time learning-to-optimize rotation averaging graph optimizer with a tiny size deployed for real-world applications. RAGO outperforms previous traditional and deep methods on real-world and synthetic datasets. The code is available at github.com/sfu-gruvi-3dv/RAGO | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_RAGO_Recurrent_Graph_Optimizer_for_Multiple_Rotation_Averaging_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_RAGO_Recurrent_Graph_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_RAGO_Recurrent_Graph_Optimizer_for_Multiple_Rotation_Averaging_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_RAGO_Recurrent_Graph_Optimizer_for_Multiple_Rotation_Averaging_CVPR_2022_paper.html | CVPR 2022 | null |
Arch-Graph: Acyclic Architecture Relation Predictor for Task-Transferable Neural Architecture Search | Minbin Huang, Zhijian Huang, Changlin Li, Xin Chen, Hang Xu, Zhenguo Li, Xiaodan Liang | Neural Architecture Search (NAS) aims to find efficient models for multiple tasks. Beyond seeking solutions for a single task, there are surging interests in transferring network design knowledge across multiple tasks. In this line of research, effectively modeling task correlations is vital yet highly neglected. Therefore, we propose Arch-Graph, a transferable NAS method that predicts task-specific optimal architectures with respect to given task embeddings. It leverages correlations across multiple tasks by using their embeddings as a part of the predictor's input for fast adaptation. We also formulate NAS as an architecture relation graph prediction problem, with the relational graph constructed by treating candidate architectures as nodes and their pairwise relations as edges. To enforce some basic properties such as acyclicity in the relational graph, we add additional constraints to the optimization process, converting NAS into the problem of finding a Maximal Weighted Acyclic Subgraph (MWAS). Our algorithm then strives to eliminate cycles and only establish edges in the graph if the rank results can be trusted. Through MWAS, Arch-Graph can effectively rank candidate models for each task with only a small budget to finetune the predictor. With extensive experiments on TransNAS-Bench-101, we show Arch-Graph's transferability and high sample efficiency across numerous tasks, beating many NAS methods designed for both single-task and multi-task search. It is able to find top 0.16% and 0.29% architectures on average on two search spaces under the budget of only 50 models. | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Arch-Graph_Acyclic_Architecture_Relation_Predictor_for_Task-Transferable_Neural_Architecture_Search_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_Arch-Graph_Acyclic_Architecture_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Arch-Graph_Acyclic_Architecture_Relation_Predictor_for_Task-Transferable_Neural_Architecture_Search_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Arch-Graph_Acyclic_Architecture_Relation_Predictor_for_Task-Transferable_Neural_Architecture_Search_CVPR_2022_paper.html | CVPR 2022 | null |
On Aliased Resizing and Surprising Subtleties in GAN Evaluation | Gaurav Parmar, Richard Zhang, Jun-Yan Zhu | Metrics for evaluating generative models aim to measure the discrepancy between real and generated images. The oftenused Frechet Inception Distance (FID) metric, for example, extracts "high-level" features using a deep network from the two sets. However, we find that the differences in "low-level" preprocessing, specifically image resizing and compression, can induce large variations and have unforeseen consequences. For instance, when resizing an image, e.g., with a bilinear or bicubic kernel, signal processing principles mandate adjusting prefilter width depending on the downsampling factor, to antialias to the appropriate bandwidth. However, commonly used implementations use a fixed-width prefilter, resulting in aliasing artifacts. Such aliasing leads to corruptions in the feature extraction downstream. Next, lossy compression, such as JPEG, is commonly used to reduce the file size of an image. Although designed to minimally degrade the perceptual quality of an image, the operation also produces variations downstream. Furthermore, we show that if compression is used on real training images, FID can actually improve if the generated images are also subsequently compressed. This paper shows that choices in low-level image processing have been an under-appreciated aspect of generative modeling. We identify and characterize variations in generative modeling development pipelines, provide recommendations based on signal processing principles, and release a reference implementation to facilitate future comparisons. | https://openaccess.thecvf.com/content/CVPR2022/papers/Parmar_On_Aliased_Resizing_and_Surprising_Subtleties_in_GAN_Evaluation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2104.11222 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Parmar_On_Aliased_Resizing_and_Surprising_Subtleties_in_GAN_Evaluation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Parmar_On_Aliased_Resizing_and_Surprising_Subtleties_in_GAN_Evaluation_CVPR_2022_paper.html | CVPR 2022 | null |
Lepard: Learning Partial Point Cloud Matching in Rigid and Deformable Scenes | Yang Li, Tatsuya Harada | We present Lepard, a Learning based approach for partial point cloud matching in rigid and deformable scenes. The key characteristics are the following techniques that exploit 3D positional knowledge for point cloud matching: 1) An architecture that disentangles point cloud representation into feature space and 3D position space. 2) A position encoding method that explicitly reveals 3D relative distance information through the dot product of vectors. 3) A repositioning technique that modifies the crosspoint-cloud relative positions. Ablation studies demonstrate the effectiveness of the above techniques. In rigid cases, Lepard combined with RANSAC and ICP demonstrates state-of-the-art registration recall of 93.9% / 71.3% on the 3DMatch / 3DLoMatch. In deformable cases, Lepard achieves +27.1% / +34.8% higher non-rigid feature matching recall than the prior art on our newly constructed 4DMatch / 4DLoMatch benchmark. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Lepard_Learning_Partial_Point_Cloud_Matching_in_Rigid_and_Deformable_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Lepard_Learning_Partial_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.12591 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Lepard_Learning_Partial_Point_Cloud_Matching_in_Rigid_and_Deformable_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Lepard_Learning_Partial_Point_Cloud_Matching_in_Rigid_and_Deformable_CVPR_2022_paper.html | CVPR 2022 | null |
Virtual Elastic Objects | Hsiao-yu Chen, Edith Tretschk, Tuur Stuyck, Petr Kadlecek, Ladislav Kavan, Etienne Vouga, Christoph Lassner | We present Virtual Elastic Objects (VEOs): virtual objects that not only look like their real-world counterparts but also behave like them, even when subject to novel interactions. Achieving this presents multiple challenges: not only do objects have to be captured including the physical forces acting on them, then faithfully reconstructed and rendered, but also plausible material parameters found and simulated. To create VEOs, we built a multi-view capture system that captures objects under the influence of a compressed air stream. Building on recent advances in model-free, dynamic Neural Radiance Fields, we reconstruct the objects and corresponding deformation fields. We propose to use a differentiable, particle-based simulator to use these deformation fields to find representative material parameters, which enable us to run new simulations. To render simulated objects, we devise a method for integrating the simulation results with Neural Radiance Fields. The resulting method is applicable to a wide range of scenarios: it can handle objects composed of inhomogeneous material, with very different shapes, and it can simulate interactions with other virtual objects. We present our results using a newly collected dataset of 12 objects under a variety of force fields, which will be made available upon publication. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Virtual_Elastic_Objects_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Virtual_Elastic_Objects_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.04623 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Virtual_Elastic_Objects_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Virtual_Elastic_Objects_CVPR_2022_paper.html | CVPR 2022 | null |
DiSparse: Disentangled Sparsification for Multitask Model Compression | Xinglong Sun, Ali Hassani, Zhangyang Wang, Gao Huang, Humphrey Shi | Despite the popularity of Model Compression and Multitask Learning, how to effectively compress a multitask model has been less thoroughly analyzed due to the challenging entanglement of tasks in the parameter space. In this paper, we propose DiSparse, a simple, effective, and first-of-its-kind multitask pruning and sparse training scheme. We consider each task independently by disentangling the importance measurement and take the unanimous decisions among all tasks when performing parameter pruning and selection. Our experimental results demonstrate superior performance on various configurations and settings compared to popular sparse training and pruning methods. Besides the effectiveness in compression, DiSparse also provides a powerful tool to the multitask learning community. Surprisingly, we even observed better performance than some dedicated multitask learning methods in several cases despite the high model sparsity enforced by DiSparse. We analyzed the pruning masks generated with DiSparse and observed strikingly similar sparse network architecture identified by each task even before the training starts. We also observe the existence of a "watershed" layer where the task relatedness sharply drops, implying no benefits in continued parameters sharing. Our code and models will be available at: https://github.com/SHI-Labs/DiSparse-Multitask-Model-Compression. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_DiSparse_Disentangled_Sparsification_for_Multitask_Model_Compression_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sun_DiSparse_Disentangled_Sparsification_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_DiSparse_Disentangled_Sparsification_for_Multitask_Model_Compression_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_DiSparse_Disentangled_Sparsification_for_Multitask_Model_Compression_CVPR_2022_paper.html | CVPR 2022 | null |
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference | Shell Xu Hu, Da Li, Jan Stühmer, Minyoung Kim, Timothy M. Hospedales | Few-shot learning (FSL) is an important and topical problem in computer vision that has motivated extensive research into numerous methods spanning from sophisticated meta-learning methods to simple transfer learning baselines. We seek to push the limits of a simple-but-effective pipeline for real-world few-shot image classification in practice. To this end, we explore few-shot learning from the perspective of neural architecture, as well as a three stage pipeline of pre-training on external data, meta-training with labelled few-shot tasks, and task-specific fine-tuning on unseen tasks. We investigate questions such as: (1) How pre-training on external data benefits FSL? (2) How state of the art transformer architectures can be exploited? and (3) How to best exploit fine-tuning? Ultimately, we show that a simple transformer-based pipeline yields surprisingly good performance on standard benchmarks such as Mini-ImageNet, CIFAR-FS, CDFSL and Meta-Dataset. Our code is available at https://hushell.github.io/pmf. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hu_Pushing_the_Limits_of_Simple_Pipelines_for_Few-Shot_Learning_External_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hu_Pushing_the_Limits_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Pushing_the_Limits_of_Simple_Pipelines_for_Few-Shot_Learning_External_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Pushing_the_Limits_of_Simple_Pipelines_for_Few-Shot_Learning_External_CVPR_2022_paper.html | CVPR 2022 | null |
Opening Up Open World Tracking | Yang Liu, Idil Esen Zulfikar, Jonathon Luiten, Achal Dave, Deva Ramanan, Bastian Leibe, Aljoša Ošep, Laura Leal-Taixé | Tracking and detecting any object, including ones never-seen-before during model training, is a crucial but elusive capability of autonomous systems. An autonomous agent that is blind to never-seen-before objects poses a safety hazard when operating in the real world - and yet this is how almost all current systems work. One of the main obstacles towards advancing tracking any object is that this task is notoriously difficult to evaluate. A benchmark that would allow us to perform an apple-to-apple comparison of existing efforts is a crucial first step towards advancing this important research field. This paper addresses this evaluation deficit and lays out the landscape and evaluation methodology for detecting and tracking both known and unknown objects in the open-world setting. We propose a new benchmark, TAO-OW: Tracking Any Object in an Open World, analyze existing efforts in multi-object tracking, and construct a baseline for this task while highlighting future challenges. We hope to open a new front in multi-object tracking research that will hopefully bring us a step closer to intelligent systems that can operate safely in the real world. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Opening_Up_Open_World_Tracking_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Opening_Up_Open_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2104.11221 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Opening_Up_Open_World_Tracking_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Opening_Up_Open_World_Tracking_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Efficient and Scalable Sharpness-Aware Minimization | Yong Liu, Siqi Mai, Xiangning Chen, Cho-Jui Hsieh, Yang You | Recently, Sharpness-Aware Minimization (SAM), which connects the geometry of the loss landscape and generalization, has demonstrated a significant performance boost on training large-scale models such as vision transformers. However, the update rule of SAM requires two sequential (non-parallelizable) gradient computations at each step, which can double the computational overhead. In this paper, we propose a novel algorithm LookSAM - that only periodically calculates the inner gradient ascent, to significantly reduce the additional training cost of SAM. The empirical results illustrate that LookSAM achieves similar accuracy gains to SAM while being tremendously faster - it enjoys comparable computational complexity with first-order optimizers such as SGD or Adam. To further evaluate the performance and scalability of LookSAM, we incorporate a layer-wise modification and perform experiments in the large-batch training scenario, which is more prone to converge to sharp local minima. Equipped with the proposed algorithms, we are the first to successfully scale up the batch size when training Vision Transformers (ViTs). With a 64k batch size, we are able to train ViTs from scratch in minutes while maintaining competitive performance. The code is available here: https://github.com/yong-6/LookSAM | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Towards_Efficient_and_Scalable_Sharpness-Aware_Minimization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Towards_Efficient_and_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.02714 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Towards_Efficient_and_Scalable_Sharpness-Aware_Minimization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Towards_Efficient_and_Scalable_Sharpness-Aware_Minimization_CVPR_2022_paper.html | CVPR 2022 | null |
VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention | Shengheng Deng, Zhihao Liang, Lin Sun, Kui Jia | Detecting objects from LiDAR point clouds is of tremendous significance in autonomous driving. In spite of good progress, accurate and reliable 3D detection is yet to be achieved due to the sparsity and irregularity of LiDAR point clouds. Among existing strategies, multi-view methods have shown great promise by leveraging the more comprehensive information from both bird's eye view (BEV) and range view (RV). These multi-view methods either refine the proposals predicted from single view via fused features, or fuse the features without considering the global spatial context; their performance is limited consequently. In this paper, we propose to adaptively fuse multi-view features in a global spatial context via Dual Cross-VIew SpaTial Attention (VISTA). The proposed VISTA is a novel plug-and-play fusion module, wherein the multi-layer perceptron widely adopted in standard attention modules is replaced with a convolutional one. Thanks to the learned attention mechanism, VISTA can produce fused features of high quality for prediction of proposals. We decouple the classification and regression tasks in VISTA, and an additional constraint of attention variance is applied that enables the attention module to focus on specific targets instead of generic points. We conduct thorough experiments on the benchmarks of nuScenes and Waymo; results confirm the efficacy of our designs. At the time of submission, our method achieves 63.0% in overall mAP and 69.8% in NDS on the nuScenes benchmark, outperforming all published methods by up to 24% in safety-crucial categories such as cyclist. | https://openaccess.thecvf.com/content/CVPR2022/papers/Deng_VISTA_Boosting_3D_Object_Detection_via_Dual_Cross-VIew_SpaTial_Attention_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Deng_VISTA_Boosting_3D_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09704 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Deng_VISTA_Boosting_3D_Object_Detection_via_Dual_Cross-VIew_SpaTial_Attention_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Deng_VISTA_Boosting_3D_Object_Detection_via_Dual_Cross-VIew_SpaTial_Attention_CVPR_2022_paper.html | CVPR 2022 | null |
Rethinking Deep Face Restoration | Yang Zhao, Yu-Chuan Su, Chun-Te Chu, Yandong Li, Marius Renn, Yukun Zhu, Changyou Chen, Xuhui Jia | A model that can authentically restore a low-quality face image to a high-quality one can benefit many applications. While existing approaches for face restoration make significant progress in generating high-quality faces, they often fail to preserve facial features and cannot authentically reconstruct the faces. Because the human visual system is very sensitive to faces, even minor facial changes may alter the identity and significantly degrade the perceptual quality. In this work, we argue the problems of existing models can be traced down to the two sub-tasks of the face restoration problem, i.e. face generation and face reconstruction, and the fragile balance between them. Based on the observation, we propose a new face restoration model that improves both generation and reconstruction by learning a stochastic model and enhancing the latent features respectively. Furthermore, we adapt the number of skip connections for a better balance between the two sub-tasks. Besides the model improvement, we also introduce a new evaluation metric for measuring models' ability to preserve the identity in the restored faces. Extensive experiments demonstrate that our model achieves state-of-the-art performance on multiple face restoration benchmarks. The user study shows that our model produces higher quality faces while better preserving the identity 86.4% of the time compared with the best performing baselines. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_Rethinking_Deep_Face_Restoration_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhao_Rethinking_Deep_Face_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Rethinking_Deep_Face_Restoration_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Rethinking_Deep_Face_Restoration_CVPR_2022_paper.html | CVPR 2022 | null |
OSSO: Obtaining Skeletal Shape From Outside | Marilyn Keller, Silvia Zuffi, Michael J. Black, Sergi Pujades | We address the problem of inferring the anatomic skeleton of a person, in an arbitrary pose, from the 3D surface of the body; i.e. we predict the inside (bones) from the outside (skin). This has many applications in medicine and biomechanics. Existing state-of-the-art biomechanical skeletons are detailed but do not easily generalize to new subjects. Additionally, computer vision and graphics methods that predict skeletons are typically heuristic, not learned from data, do not leverage the full 3D body surface, and are not validated against ground truth. To our knowledge, our system, called OSSO (Obtaining Skeletal Shape from Outside), is the first to learn the mapping from the 3D body surface to the internal skeleton from real data. We do so using 1000 male and 1000 female dual-energy X-ray absorptiometry (DXA) scans. To these, we fit a parametric 3D body shape model (STAR) to capture the body surface and a novel part-based 3D skeleton model to capture the bones. This provides inside/outside training pairs. We model the statistical variation of full skeletons using PCA in a pose-normalized space and train a regressor from body shape parameters to skeleton shape parameters. Given an arbitrary 3D body shape and pose, OSSO predicts a realistic skeleton inside. In contrast to previous work, we evaluate the accuracy of the skeleton shape quantitatively on held out DXA scans, outperforming the state-of-the art. We also show 3D skeleton prediction from varied and challenging 3D bodies. The code to infer a skeleton from a body shape is available at https://osso.is.tue.mpg.de, and the dataset of paired outer surface (skin) and skeleton (bone) meshes is available as a Biobank Returned Dataset. This research has been conducted using the UK Biobank Resource. | https://openaccess.thecvf.com/content/CVPR2022/papers/Keller_OSSO_Obtaining_Skeletal_Shape_From_Outside_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Keller_OSSO_Obtaining_Skeletal_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.10129 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Keller_OSSO_Obtaining_Skeletal_Shape_From_Outside_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Keller_OSSO_Obtaining_Skeletal_Shape_From_Outside_CVPR_2022_paper.html | CVPR 2022 | null |
Temporal Alignment Networks for Long-Term Video | Tengda Han, Weidi Xie, Andrew Zisserman | The objective of this paper is a temporal alignment network that ingests long term video sequences, and associated text sentences, in order to: (1) determine if a sentence is alignable with the video; and (2) if it is alignable, then determine its alignment. The challenge is to train such networks from large-scale datasets, such as HowTo100M, where the associated text sentences have significant noise, and are only weakly aligned when relevant. Apart from proposing the alignment network, we also make four contributions: (i) we describe a novel co-training method that enables to denoise and train on raw instructional videos without using manual annotation, despite the considerable noise; (ii) to benchmark the alignment performance, we manually curate a 10-hour subset of HowTo100M, totalling 80 videos, with sparse temporal descriptions. Our proposed model, trained on HowTo100M, outperforms strong baselines (CLIP, MIL-NCE) on this alignment dataset by a significant margin; (iii) we apply the trained model in the zero-shot settings to multiple downstream video understanding tasks and achieve state-of-the-art results, including text-video retrieval on YouCook2, and weakly supervised video action segmentation on Breakfast-Action. (iv) we use the automatically-aligned HowTo100M annotations for end-to-end finetuning of the backbone model, and obtain improved performance on downstream action recognition tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Han_Temporal_Alignment_Networks_for_Long-Term_Video_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Han_Temporal_Alignment_Networks_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2204.02968 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Han_Temporal_Alignment_Networks_for_Long-Term_Video_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Han_Temporal_Alignment_Networks_for_Long-Term_Video_CVPR_2022_paper.html | CVPR 2022 | null |
Few-Shot Head Swapping in the Wild | Changyong Shu, Hemao Wu, Hang Zhou, Jiaming Liu, Zhibin Hong, Changxing Ding, Junyu Han, Jingtuo Liu, Errui Ding, Jingdong Wang | The head swapping task aims at flawlessly placing a source head onto a target body, which is of great importance to various entertainment scenarios. While face swapping has drawn much attention in the community, the task of head swapping has rarely been explored, particularly under the few-shot setting. It is inherently challenging due to its unique needs in head modeling and background blending. In this paper, we present the Head Swapper (HeSer), which achieves few-shot head swapping in the wild through two dedicated designed modules. Firstly, a Head2Head Aligner is devised to holistically migrate position and expression information from the target to the source head by examining multi-scale information. Secondly, to tackle the challenges of skin color variations and head-background mismatches, a Head2Scene Blender is introduced to simultaneously modify facial skin color and fill mismatched gaps on the background around the head. Particularly, seamless blending is achieved through a semantic-guided exemplar warping procedure. User studies and experimental results demonstrate that the proposed method produces superior head swapping results on a variety of scenes. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shu_Few-Shot_Head_Swapping_in_the_Wild_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shu_Few-Shot_Head_Swapping_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2204.13100 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shu_Few-Shot_Head_Swapping_in_the_Wild_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shu_Few-Shot_Head_Swapping_in_the_Wild_CVPR_2022_paper.html | CVPR 2022 | null |
A Study on the Distribution of Social Biases in Self-Supervised Learning Visual Models | Kirill Sirotkin, Pablo Carballeira, Marcos Escudero-Viñolo | Deep neural networks are efficient at learning the data distribution if it is sufficiently sampled. However, they can be strongly biased by non-relevant factors implicitly incorporated in the training data. These include operational biases, such as ineffective or uneven data sampling, but also ethical concerns, as the social biases are implicitly present--even inadvertently, in the training data or explicitly defined in unfair training schedules. In tasks having impact on human processes, the learning of social biases may produce discriminatory, unethical and untrustworthy consequences. It is often assumed that social biases stem from supervised learning on labelled data, and thus, Self-Supervised Learning (SSL) wrongly appears as an efficient and bias-free solution, as it does not require labelled data. However, it was recently proven that a popular SSL method also incorporates biases. In this paper, we study the biases of a varied set of SSL visual models, trained using ImageNet data, using a method and dataset designed by psychological experts to measure social biases. We show that there is a correlation between the type of the SSL model and the number of biases that it incorporates. Furthermore, the results also suggest that this number does not strictly depend on the model's accuracy and changes throughout the network. Finally, we conclude that a careful SSL model selection process can reduce the number of social biases in the deployed model, whilst keeping high performance. The code is available at https://github.com/vpulab/SB-SSL. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sirotkin_A_Study_on_the_Distribution_of_Social_Biases_in_Self-Supervised_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sirotkin_A_Study_on_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sirotkin_A_Study_on_the_Distribution_of_Social_Biases_in_Self-Supervised_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sirotkin_A_Study_on_the_Distribution_of_Social_Biases_in_Self-Supervised_CVPR_2022_paper.html | CVPR 2022 | null |
LAR-SR: A Local Autoregressive Model for Image Super-Resolution | Baisong Guo, Xiaoyun Zhang, Haoning Wu, Yu Wang, Ya Zhang, Yan-Feng Wang | Previous super-resolution (SR) approaches often formulate SR as a regression problem and pixel wise restoration, which leads to a blurry and unreal SR output. Recent works combine adversarial loss with pixel-wise loss to train a GAN-based model or introduce normalizing flows into SR problems to generate more realistic images. As another powerful generative approach, autoregressive (AR) model has not been noticed in low level tasks due to its limitation. Based on the fact that given the structural information, the textural details in the natural images are locally related without long term dependency, in this paper we propose a novel autoregressive model-based SR approach, namely LAR-SR, which can efficiently generate realistic SR images using a novel local autoregressive (LAR) module. The proposed LAR module can sample all the patches of textural components in parallel, which greatly reduces the time consumption. In addition to high time efficiency, it is also able to leverage contextual information of pixels and can be optimized with a consistent loss. Experimental results on the widely-used datasets show that the proposed LAR-SR approach achieves superior performance on the visual quality and quantitative metrics compared with other generative models such as GAN, Flow, and is competitive with the mixture generative model. | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_LAR-SR_A_Local_Autoregressive_Model_for_Image_Super-Resolution_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_LAR-SR_A_Local_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_LAR-SR_A_Local_Autoregressive_Model_for_Image_Super-Resolution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_LAR-SR_A_Local_Autoregressive_Model_for_Image_Super-Resolution_CVPR_2022_paper.html | CVPR 2022 | null |
Bayesian Invariant Risk Minimization | Yong Lin, Hanze Dong, Hao Wang, Tong Zhang | Generalization under distributional shift is an open challenge for machine learning. Invariant Risk Minimization (IRM) is a promising framework to tackle this issue by extracting invariant features. However, despite the potential and popularity of IRM, recent works have reported negative results of it on deep models. We argue that the failure can be primarily attributed to deep models' tendency to overfit the data. Specifically, our theoretical analysis shows that IRM degenerates to empirical risk minimization (ERM) when overfitting occurs. Our empirical evidence also provides supports: IRM methods that work well in typical settings significantly deteriorate even if we slightly enlarge the model size or lessen the training data. To alleviate this issue, we propose Bayesian Invariant Risk Minimization (BIRM) by introducing Bayesian inference into the IRM. The key motivation is to estimate the penalty of IRM based on the posterior distribution of classifiers (as opposed to a single classifier), which is much less prone to overfitting. Extensive experimental results on four datasets demonstrate that BIRM consistently outperforms the existing IRM baselines significantly. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_Bayesian_Invariant_Risk_Minimization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lin_Bayesian_Invariant_Risk_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_Bayesian_Invariant_Risk_Minimization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_Bayesian_Invariant_Risk_Minimization_CVPR_2022_paper.html | CVPR 2022 | null |
Democracy Does Matter: Comprehensive Feature Mining for Co-Salient Object Detection | Siyue Yu, Jimin Xiao, Bingfeng Zhang, Eng Gee Lim | Co-salient object detection, with the target of detecting co-existed salient objects among a group of images, is gaining popularity. Recent works use the attention mechanism or extra information to aggregate common co-salient features, leading to incomplete even incorrect responses for target objects. In this paper, we aim to mine comprehensive co-salient features with democracy and reduce background interference without introducing any extra information. To achieve this, we design a democratic prototype generation module to generate democratic response maps, covering sufficient co-salient regions and thereby involving more shared attributes of co-salient objects. Then a comprehensive prototype based on the response maps can be generated as a guide for final prediction. To suppress the noisy background information in the prototype, we propose a self-contrastive learning module, where both positive and negative pairs are formed without relying on additional classification information. Besides, we also design a democratic feature enhancement module to further strengthen the co-salient features by readjusting attention values. Extensive experiments show that our model obtains better performance than previous state-of-the-art methods, especially on challenging real-world cases (e.g., for CoCA, we obtain a gain of 2.0% for MAE, 5.4% for maximum F-measure, 2.3% for maximum E-measure, and 3.7% for S-measure) under the same settings. Code will be released soon. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_Democracy_Does_Matter_Comprehensive_Feature_Mining_for_Co-Salient_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yu_Democracy_Does_Matter_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.05787 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Democracy_Does_Matter_Comprehensive_Feature_Mining_for_Co-Salient_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Democracy_Does_Matter_Comprehensive_Feature_Mining_for_Co-Salient_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint | Jiaxian Guo, Jiachen Li, Huan Fu, Mingming Gong, Kun Zhang, Dacheng Tao | Unsupervised image-to-image (I2I) translation aims to learn a domain mapping function that can preserve the semantics of the input images without paired data. However, because the underlying semantics distributions in the source and target domains are often mismatched, current distribution matching-based methods may distort the semantics when matching distributions, resulting in the inconsistency between the input and translated images, which is known as the semantics distortion problem. In this paper, we focus on the low-level I2I translation, where the structure of images is highly related to their semantics. To alleviate semantic distortions in such translation tasks without paired supervision, we propose a novel I2I translation constraint, called Structure Consistency Constraint (SCC), to promote the consistency of image structures by reducing the randomness of color transformation in the translation process. To facilitate estimation and maximization of SCC, we propose an approximate representation of mutual information called relative Squared-loss Mutual Information (rSMI) that enjoys efficient analytic solutions. Our SCC can be easily incorporated into most existing translation models. Quantitative and qualitative comparisons on a range of low-level I2I translation tasks show that translation models with SCC outperform the original models by a significant margin with little additional computational and memory costs. | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_Alleviating_Semantics_Distortion_in_Unsupervised_Low-Level_Image-to-Image_Translation_via_Structure_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_Alleviating_Semantics_Distortion_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Alleviating_Semantics_Distortion_in_Unsupervised_Low-Level_Image-to-Image_Translation_via_Structure_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Alleviating_Semantics_Distortion_in_Unsupervised_Low-Level_Image-to-Image_Translation_via_Structure_CVPR_2022_paper.html | CVPR 2022 | null |
Doodle It Yourself: Class Incremental Learning by Drawing a Few Sketches | Ayan Kumar Bhunia, Viswanatha Reddy Gajjala, Subhadeep Koley, Rohit Kundu, Aneeshan Sain, Tao Xiang, Yi-Zhe Song | The human visual system is remarkable in learning new visual concepts from just a few examples. This is precisely the goal behind few-shot class incremental learning (FSCIL), where the emphasis is additionally placed on ensuring the model does not suffer from "forgetting". In this paper, we push the boundary further for FSCIL by addressing two key questions that bottleneck its ubiquitous application (i) can the model learn from diverse modalities other than just photo (as humans do), and (ii) what if photos are not readily accessible (due to ethical and privacy constraints). Our key innovation lies in advocating the use of sketches as a new modality for class support. The product is a "Doodle It Yourself" (DIY) FSCIL framework where the users can freely sketch a few examples of a novel class for the model to learn to recognise photos of that class. For that, we present a framework that infuses (i) gradient consensus for domain invariant learning, (ii) knowledge distillation for preserving old class information, and (iii) graph attention networks for message passing between old and novel classes. We experimentally show that sketches are better class support than text in the context of FSCIL, echoing findings elsewhere in the sketching literature. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bhunia_Doodle_It_Yourself_Class_Incremental_Learning_by_Drawing_a_Few_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bhunia_Doodle_It_Yourself_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14843 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bhunia_Doodle_It_Yourself_Class_Incremental_Learning_by_Drawing_a_Few_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bhunia_Doodle_It_Yourself_Class_Incremental_Learning_by_Drawing_a_Few_CVPR_2022_paper.html | CVPR 2022 | null |
ICON: Implicit Clothed Humans Obtained From Normals | Yuliang Xiu, Jinlong Yang, Dimitrios Tzionas, Michael J. Black | Current methods for learning realistic and animatable 3D clothed avatars need either posed 3D scans or 2D images with carefully controlled user poses. In contrast, our goal is to learn the avatar from only 2D images of people in unconstrained poses. Given a set of images, our method estimates a detailed 3D surface from each image and then combines these into an animatable avatar. Implicit functions are well suited to the first task, as they can capture details like hair or clothes. Current methods, however, are not robust to varied human poses and often produce 3D surfaces with broken or disembodied limbs, missing details, or non-human shapes. The problem is that these methods use global feature encoders that are sensitive to global pose. To address this, we propose ICON ("Implicit Clothed humans Obtained from Normals"), which uses local features. ICON has two main modules, both of which exploit the SMPL body model. First, ICON infers detailed clothed-human normals(front/back) conditioned on the SMPL normals. Second, a visibility-aware implicit surface regressor produces an iso-surface of the human occupancy field. Importantly, at inference time, a feedback loop alternates between refining the SMPL mesh using the inferred clothed normals and then refining the normals. Given multiple reconstructed frames of a subject in varied poses, we use modified SCANimate to produce an animatable avatar from them. Evaluation on the AGORA and CAPE datasets shows that ICON outperforms the state-of-the-art in reconstruction, even with heavily limited training data. Additionally, it is much more robust to out-of-distribution samples, e.g., in-the-wild poses/images and out-of-frame cropping. ICON takes a step towards pose-robust 3D clothed human reconstruction from in-the-wild images. This enables creating avatars directly from video with personalized and nature pose-dependent cloth deformation. Our models and code will be available for research. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xiu_ICON_Implicit_Clothed_Humans_Obtained_From_Normals_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xiu_ICON_Implicit_Clothed_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.09127 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xiu_ICON_Implicit_Clothed_Humans_Obtained_From_Normals_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xiu_ICON_Implicit_Clothed_Humans_Obtained_From_Normals_CVPR_2022_paper.html | CVPR 2022 | null |
Comparing Correspondences: Video Prediction With Correspondence-Wise Losses | Daniel Geng, Max Hamilton, Andrew Owens | Image prediction methods often struggle on tasks that require changing the positions of objects, such as video prediction, producing blurry images that average over the many positions that objects might occupy. In this paper, we propose a simple change to existing image similarity metrics that makes them more robust to positional errors: we match the images using optical flow, then measure the visual similarity of corresponding pixels. This change leads to crisper and more perceptually accurate predictions, and does not require modifications to the image prediction network. We apply our method to a variety of video prediction tasks, where it obtains strong performance with simple network architectures, and to the closely related task of video interpolation. Code and results are available at our webpage: https://dangeng.github.io/CorrWiseLosses | https://openaccess.thecvf.com/content/CVPR2022/papers/Geng_Comparing_Correspondences_Video_Prediction_With_Correspondence-Wise_Losses_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2104.09498 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Geng_Comparing_Correspondences_Video_Prediction_With_Correspondence-Wise_Losses_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Geng_Comparing_Correspondences_Video_Prediction_With_Correspondence-Wise_Losses_CVPR_2022_paper.html | CVPR 2022 | null |
Uni-Perceiver: Pre-Training Unified Architecture for Generic Perception for Zero-Shot and Few-Shot Tasks | Xizhou Zhu, Jinguo Zhu, Hao Li, Xiaoshi Wu, Hongsheng Li, Xiaohua Wang, Jifeng Dai | Biological intelligence systems of animals perceive the world by integrating information in different modalities and processing simultaneously for various tasks. In contrast, current machine learning research follows a task-specific paradigm, leading to inefficient collaboration between tasks and high marginal costs of developing perception models for new tasks. In this paper, we present a generic perception architecture named Uni-Perceiver, which processes a variety of modalities and tasks with unified modeling and shared parameters. Specifically, Uni-Perceiver encodes different task inputs and targets from arbitrary modalities into a unified representation space with a modality-agnostic Transformer encoder and lightweight modality-specific tokenizers. Different perception tasks are modeled as the same formulation, that is, finding the maximum likelihood target for each input through the similarity of their representations. The model is pre-trained on several uni-modal and multi-modal tasks, and evaluated on a variety of downstream tasks, including novel tasks that did not appear in the pre-training stage. Results show that our pre-trained model without any tuning can achieve reasonable performance even on novel tasks. The performance can be improved to a level close to state-of-the-art methods by conducting prompt tuning on 1% of downstream task data. Full-data fine-tuning further delivers results on par with or better than state-of-the-art results. Code and pre-trained weights shall be released. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_Uni-Perceiver_Pre-Training_Unified_Architecture_for_Generic_Perception_for_Zero-Shot_and_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_Uni-Perceiver_Pre-Training_Unified_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Uni-Perceiver_Pre-Training_Unified_Architecture_for_Generic_Perception_for_Zero-Shot_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Uni-Perceiver_Pre-Training_Unified_Architecture_for_Generic_Perception_for_Zero-Shot_and_CVPR_2022_paper.html | CVPR 2022 | null |
The Auto Arborist Dataset: A Large-Scale Benchmark for Multiview Urban Forest Monitoring Under Domain Shift | Sara Beery, Guanhang Wu, Trevor Edwards, Filip Pavetic, Bo Majewski, Shreyasee Mukherjee, Stanley Chan, John Morgan, Vivek Rathod, Jonathan Huang | Generalization to novel domains is a fundamental challenge for computer vision. Near-perfect accuracy on benchmarks is common, but these models do not work as expected when deployed outside of the training distribution. To build computer vision systems that truly solve real-world problems at global scale, we need benchmarks that fully capture real-world complexity, including geographic domain shift, long-tailed distributions, and data noise. We propose urban forest monitoring as an ideal testbed for studying and improving upon these computer vision challenges, while simultaneously working towards filling a crucial environmental and societal need. Urban forests provide significant benefits to urban societies (e.g., cleaner air and water, carbon sequestration, and energy savings among others). However, planning and maintaining these forests is expensive. One particularly costly aspect of urban forest management is monitoring the existing trees in a city: e.g., tracking tree locations, species, and health. Monitoring efforts are currently based on tree censuses built by human experts, costing cities millions of dollars per census and thus collected infrequently. Previous investigations into automating urban forest monitoring focused on small datasets from single cities, covering only common categories. To address these shortcomings, we introduce a new large-scale dataset that joins public tree censuses from 23 cities with a large collection of street level and aerial imagery. Our Auto Arborist dataset contains over 2.5M trees and 344 genera and is >2 orders of magnitude larger than the closest dataset in the literature. We introduce baseline results on our dataset across modalities as well as metrics for the detailed analysis of generalization with respect to geographic distribution shifts, vital for such a system to be deployed at-scale. | https://openaccess.thecvf.com/content/CVPR2022/papers/Beery_The_Auto_Arborist_Dataset_A_Large-Scale_Benchmark_for_Multiview_Urban_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Beery_The_Auto_Arborist_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Beery_The_Auto_Arborist_Dataset_A_Large-Scale_Benchmark_for_Multiview_Urban_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Beery_The_Auto_Arborist_Dataset_A_Large-Scale_Benchmark_for_Multiview_Urban_CVPR_2022_paper.html | CVPR 2022 | null |
On the Instability of Relative Pose Estimation and RANSAC's Role | Hongyi Fan, Joe Kileel, Benjamin Kimia | Relative pose estimation using the 5-point or 7-point Random Sample Consensus (RANSAC) algorithms can fail even when no outliers are present and there are enough inliers to support a hypothesis. These cases arise due to numerical instability of the 5- and 7-point minimal problems. This paper characterizes these instabilities, both in terms of minimal world scene configurations that lead to infinite condition number in epipolar estimation, and also in terms of the related minimal image feature pair correspondence configurations. The instability is studied in the context of a novel framework for analyzing the conditioning of minimal problems in multiview geometry, based on Riemannian manifolds. Experiments with synthetic and real-world data reveal that RANSAC does not only serve to filter out outliers, but RANSAC also selects for well-conditioned image data, sufficiently separated from the ill-posed locus that our theory predicts. These findings suggest that, in future work, one could try to accelerate and increase the success of RANSAC by testing only well-conditioned image data. | https://openaccess.thecvf.com/content/CVPR2022/papers/Fan_On_the_Instability_of_Relative_Pose_Estimation_and_RANSACs_Role_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fan_On_the_Instability_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_On_the_Instability_of_Relative_Pose_Estimation_and_RANSACs_Role_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_On_the_Instability_of_Relative_Pose_Estimation_and_RANSACs_Role_CVPR_2022_paper.html | CVPR 2022 | null |
Shape From Polarization for Complex Scenes in the Wild | Chenyang Lei, Chenyang Qi, Jiaxin Xie, Na Fan, Vladlen Koltun, Qifeng Chen | We present a new data-driven approach with physics-based priors to scene-level normal estimation from a single polarization image. Existing shape from polarization (SfP) works mainly focus on estimating the normal of a single object rather than complex scenes in the wild. A key barrier to high-quality scene-level SfP is the lack of real-world SfP data in complex scenes. Hence, we contribute the first real-world scene-level SfP dataset with paired input polarization images and ground-truth normal maps. Then we propose a learning-based framework with a multi-head self-attention module and viewing encoding, which is designed to handle increasing polarization ambiguities caused by complex materials and non-orthographic projection in scene-level SfP. Our trained model can be generalized to far-field outdoor scenes as the relationship between polarized light and surface normals is not affected by distance. Experimental results demonstrate that our approach significantly outperforms existing SfP models on two datasets. Our dataset and source code will be publicly available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lei_Shape_From_Polarization_for_Complex_Scenes_in_the_Wild_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2112.11377 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lei_Shape_From_Polarization_for_Complex_Scenes_in_the_Wild_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lei_Shape_From_Polarization_for_Complex_Scenes_in_the_Wild_CVPR_2022_paper.html | CVPR 2022 | null |
Real-Time, Accurate, and Consistent Video Semantic Segmentation via Unsupervised Adaptation and Cross-Unit Deployment on Mobile Device | Hyojin Park, Alan Yessenbayev, Tushar Singhal, Navin Kumar Adhikari, Yizhe Zhang, Shubhankar Mangesh Borse, Hong Cai, Nilesh Prasad Pandey, Fei Yin, Frank Mayer, Balaji Calidas, Fatih Porikli | This demonstration showcases our innovations on efficient, accurate, and temporally consistent video semantic segmentation on mobile device. We employ our test-time unsupervised scheme, AuxAdapt, to enable the segmentation model to adapt to a given video in an online manner. More specifically, we leverage a small auxiliary network to perform weight updates and keep the large, main segmentation network frozen. This significantly reduces the computational cost of adaptation when compared to previous methods (e.g., Tent, DVP), and at the same time, prevents catastrophic forgetting. By running AuxAdapt, we can considerably improve the temporal consistency of video segmentation while maintaining the accuracy. We demonstrate how to efficiently deploy our adaptive video segmentation algorithm on a smartphone powered by a Snapdragon Mobile Platform. Rather than simply running the entire algorithm on the GPU, we adopt a cross-unit deployment strategy. The main network, which will be frozen during test time, will perform inferences on a highly optimized AI accelerator unit, while the small auxiliary network, which will be updated on the fly, will run forward passes and back-propagations on the GPU. Such a deployment scheme best utilizes the available processing power on the smartphone and enables real-time operation of our adaptive video segmentation algorithm. We provide example videos in supplementary material. | https://openaccess.thecvf.com/content/CVPR2022/papers/Park_Real-Time_Accurate_and_Consistent_Video_Semantic_Segmentation_via_Unsupervised_Adaptation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Park_Real-Time_Accurate_and_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Park_Real-Time_Accurate_and_Consistent_Video_Semantic_Segmentation_via_Unsupervised_Adaptation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Park_Real-Time_Accurate_and_Consistent_Video_Semantic_Segmentation_via_Unsupervised_Adaptation_CVPR_2022_paper.html | CVPR 2022 | null |
SNUG: Self-Supervised Neural Dynamic Garments | Igor Santesteban, Miguel A. Otaduy, Dan Casas | We present a self-supervised method to learn dynamic 3D deformations of garments worn by parametric human bodies. State-of-the-art data-driven approaches to model 3D garment deformations are trained using supervised strategies that require large datasets, usually obtained by expensive physics-based simulation methods or professional multi-camera capture setups. In contrast, we propose a new training scheme that removes the need for ground-truth samples, enabling self-supervised training of dynamic 3D garment deformations. Our key contribution is to realize that physics-based deformation models, traditionally solved in a frame-by-frame basis by implicit integrators, can be recasted as an optimization problem. We leverage such optimization-based scheme to formulate a set of physics-based loss terms that can be used to train neural networks without precomputing ground-truth data. This allows us to learn models for interactive garments, including dynamic deformations and fine wrinkles, with two orders of magnitude speed up in training time compared to state-of-the-art supervised methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Santesteban_SNUG_Self-Supervised_Neural_Dynamic_Garments_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Santesteban_SNUG_Self-Supervised_Neural_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2204.02219 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Santesteban_SNUG_Self-Supervised_Neural_Dynamic_Garments_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Santesteban_SNUG_Self-Supervised_Neural_Dynamic_Garments_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation | Binhui Xie, Longhui Yuan, Shuang Li, Chi Harold Liu, Xinjing Cheng | Self-training has greatly facilitated domain adaptive semantic segmentation, which iteratively generates pseudo labels on unlabeled target data and retrains the network. However, realistic segmentation datasets are highly imbalanced, pseudo labels are typically biased to the majority classes and basically noisy, leading to an error-prone and suboptimal model. In this paper, we propose a simple region-based active learning approach for semantic segmentation under a domain shift, aiming to automatically query a small partition of image regions to be labeled while maximizing segmentation performance. Our algorithm, Region Impurity and Prediction Uncertainty (RIPU), introduces a new acquisition strategy characterizing the spatial adjacency of image regions along with the prediction confidence. We show that the proposed region-based selection strategy makes more efficient use of a limited budget than image-based or point-based counterparts. Further, we enforce local prediction consistency between a pixel and its nearest neighbors on a source image. Alongside, we develop a negative learning loss to make the features more discriminative. Extensive experiments demonstrate that our method only requires very few annotations to almost reach the supervised performance and substantially outperforms state-of-the-art methods. The code is available at https://github.com/BIT-DA/RIPU. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xie_Towards_Fewer_Annotations_Active_Learning_via_Region_Impurity_and_Prediction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xie_Towards_Fewer_Annotations_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.12940 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_Towards_Fewer_Annotations_Active_Learning_via_Region_Impurity_and_Prediction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_Towards_Fewer_Annotations_Active_Learning_via_Region_Impurity_and_Prediction_CVPR_2022_paper.html | CVPR 2022 | null |
Glass Segmentation Using Intensity and Spectral Polarization Cues | Haiyang Mei, Bo Dong, Wen Dong, Jiaxi Yang, Seung-Hwan Baek, Felix Heide, Pieter Peers, Xiaopeng Wei, Xin Yang | Transparent and semi-transparent materials pose significant challenges for existing scene understanding and segmentation algorithms due to their lack of RGB texture which impedes the extraction of meaningful features. In this work, we exploit that the light-matter interactions on glass materials provide unique intensity-polarization cues for each observed wavelength of light. We present a novel learning-based glass segmentation network that leverages both trichromatic (RGB) intensities as well as trichromatic linear polarization cues from a single photograph captured without making any assumption on the polarization state of the illumination. Our novel network architecture dynamically fuses and weights both the trichromatic color and polarization cues using a novel global-guidance and multi-scale self-attention module, and leverages global cross-domain contextual information to achieve robust segmentation. We train and extensively validate our segmentation method on a new large-scale RGB-Polarization dataset (RGBP-Glass), and demonstrate that our method outperforms state-of-the-art segmentation approaches by a significant margin. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mei_Glass_Segmentation_Using_Intensity_and_Spectral_Polarization_Cues_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mei_Glass_Segmentation_Using_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mei_Glass_Segmentation_Using_Intensity_and_Spectral_Polarization_Cues_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mei_Glass_Segmentation_Using_Intensity_and_Spectral_Polarization_Cues_CVPR_2022_paper.html | CVPR 2022 | null |
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding | Mohamed Afham, Isuru Dissanayake, Dinithi Dissanayake, Amaya Dharmasiri, Kanchana Thilakarathna, Ranga Rodrigo | Manual annotation of large-scale point cloud dataset for varying tasks such as 3D object classification, segmentation and detection is often laborious owing to the irregular structure of point clouds. Self-supervised learning, which operates without any human labeling, is a promising approach to address this issue. We observe in the real world that humans are capable of mapping the visual concepts learnt from 2D images to understand the 3D world. Encouraged by this insight, we propose CrossPoint, a simple cross-modal contrastive learning approach to learn transferable 3D point cloud representations. It enables a 3D-2D correspondence of objects by maximizing agreement between point clouds and the corresponding rendered 2D image in the invariant space, while encouraging invariance to transformations in the point cloud modality. Our joint training objective combines the feature correspondences within and across modalities, thus ensembles a rich learning signal from both 3D point cloud and 2D image modalities in a self-supervised fashion. Experimental results show that our approach outperforms the previous unsupervised learning methods on a diverse range of downstream tasks including 3D object classification and segmentation. Further, the ablation studies validates the potency of our approach for a better point cloud understanding. Code and pretrained models are available at https://github.com/MohamedAfham/CrossPoint. | https://openaccess.thecvf.com/content/CVPR2022/papers/Afham_CrossPoint_Self-Supervised_Cross-Modal_Contrastive_Learning_for_3D_Point_Cloud_Understanding_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.00680 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Afham_CrossPoint_Self-Supervised_Cross-Modal_Contrastive_Learning_for_3D_Point_Cloud_Understanding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Afham_CrossPoint_Self-Supervised_Cross-Modal_Contrastive_Learning_for_3D_Point_Cloud_Understanding_CVPR_2022_paper.html | CVPR 2022 | null |
Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignment | Jiayu Xiao, Liang Li, Chaofei Wang, Zheng-Jun Zha, Qingming Huang | Training a generative adversarial network (GAN) with limited data has been a challenging task. A feasible solution is to start with a GAN well-trained on a large scale source domain and adapt it to the target domain with a few samples, termed as few shot generative model adaption. However, existing methods are prone to model overfitting and collapse in extremely few shot setting (less than 10). To solve this problem, we propose a relaxed spatial structural alignment (RSSA) method to calibrate the target generative models during the adaption. We design a cross-domain spatial structural consistency loss comprising the self-correlation and disturbance correlation consistency loss. It helps align the spatial structural information between the synthesis image pairs of the source and target domains. To relax the cross-domain alignment, we compress the original latent space of generative models to a subspace. Image pairs generated from the subspace are pulled closer. Qualitative and quantitative experiments show that our method consistently surpasses the state-of-the-art methods in few shot setting. Our source code: https://github.com/StevenShaw1999/RSSA. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xiao_Few_Shot_Generative_Model_Adaption_via_Relaxed_Spatial_Structural_Alignment_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.04121 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xiao_Few_Shot_Generative_Model_Adaption_via_Relaxed_Spatial_Structural_Alignment_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xiao_Few_Shot_Generative_Model_Adaption_via_Relaxed_Spatial_Structural_Alignment_CVPR_2022_paper.html | CVPR 2022 | null |
Target-Relevant Knowledge Preservation for Multi-Source Domain Adaptive Object Detection | Jiaxi Wu, Jiaxin Chen, Mengzhe He, Yiru Wang, Bo Li, Bingqi Ma, Weihao Gan, Wei Wu, Yali Wang, Di Huang | Domain adaptive object detection (DAOD) is a promising way to alleviate performance drop of detectors in new scenes. Albeit great effort made in single source domain adaptation, a more generalized task with multiple source domains remains not being well explored, due to knowledge degradation during their combination. To address this issue, we propose a novel approach, namely target-relevant knowledge preservation (TRKP), to unsupervised multi-source DAOD. Specifically, TRKP adopts the teacher-student framework, where the multi-head teacher network is built to extract knowledge from labeled source domains and guide the student network to learn detectors in unlabeled target domain. The teacher network is further equipped with an adversarial multi-source disentanglement (AMSD) module to preserve source domain-specific knowledge and simultaneously perform cross-domain alignment. Besides, a holistic target-relevant mining (HTRM) scheme is developed to re-weight the source images according to the source-target relevance. By this means, the teacher network is enforced to capture target-relevant knowledge, thus benefiting decreasing domain shift when mentoring object detection in the target domain. Extensive experiments are conducted on various widely used benchmarks with new state-of-the-art scores reported, highlighting the effectiveness. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Target-Relevant_Knowledge_Preservation_for_Multi-Source_Domain_Adaptive_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_Target-Relevant_Knowledge_Preservation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.07964 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Target-Relevant_Knowledge_Preservation_for_Multi-Source_Domain_Adaptive_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Target-Relevant_Knowledge_Preservation_for_Multi-Source_Domain_Adaptive_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Pyramid Grafting Network for One-Stage High Resolution Saliency Detection | Chenxi Xie, Changqun Xia, Mingcan Ma, Zhirui Zhao, Xiaowu Chen, Jia Li | Recent salient object detection (SOD) methods based on deep neural network have achieved remarkable performance. However, most of existing SOD models designed for low-resolution input perform poorly on high-resolution images due to the contradiction between the sampling depth and the receptive field size. Aiming at resolving this contradiction, we propose a novel one-stage framework called Pyramid Grafting Network (PGNet), using transformer and CNN backbone to extract features from different resolution images independently and then graft the features from transformer branch to CNN branch. An attention-based Cross-Model Grafting Module (CMGM) is proposed to enable CNN branch to combine broken detailed information more holistically, guided by different source feature during decoding process. Moreover, we design an Attention Guided Loss (AGL) to explicitly supervise the attention matrix generated by CMGM to help the network better interact with the attention from different models. We contribute a new Ultra-High-Resolution Saliency Detection dataset UHRSD, containing 5,920 images at 4K-8K resolutions. To our knowledge, it is the largest dataset in both quantity and resolution for high-resolution SOD task, which can be used for training and testing in future research. Sufficient experiments on UHRSD and widely-used SOD datasets demonstrate that our method achieves superior performance compared to the state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xie_Pyramid_Grafting_Network_for_One-Stage_High_Resolution_Saliency_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xie_Pyramid_Grafting_Network_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.05041 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_Pyramid_Grafting_Network_for_One-Stage_High_Resolution_Saliency_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_Pyramid_Grafting_Network_for_One-Stage_High_Resolution_Saliency_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
A Style-Aware Discriminator for Controllable Image Translation | Kunhee Kim, Sanghun Park, Eunyeong Jeon, Taehun Kim, Daijin Kim | Current image-to-image translations do not control the output domain beyond the classes used during training, nor do they interpolate between different domains well, leading to implausible results. This limitation largely arises because labels do not consider the semantic distance. To mitigate such problems, we propose a style-aware discriminator that acts as a critic as well as a style encoder to provide conditions. The style-aware discriminator learns a controllable style space using prototype-based self-supervised learning and simultaneously guides the generator. Experiments on multiple datasets verify that the proposed model outperforms current state-of-the-art image-to-image translation methods. In contrast with current methods, the proposed approach supports various applications, including style interpolation, content transplantation, and local image translation. The code is available at github.com/kunheek/style-aware-discriminator. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_A_Style-Aware_Discriminator_for_Controllable_Image_Translation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_A_Style-Aware_Discriminator_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15375 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_A_Style-Aware_Discriminator_for_Controllable_Image_Translation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_A_Style-Aware_Discriminator_for_Controllable_Image_Translation_CVPR_2022_paper.html | CVPR 2022 | null |
Non-Iterative Recovery From Nonlinear Observations Using Generative Models | Jiulong Liu, Zhaoqiang Liu | In this paper, we aim to estimate the direction of an underlying signal from its nonlinear observations following the semi-parametric single index model (SIM). Unlike for conventional compressed sensing where the signal is assumed to be sparse, we assume that the signal lies in the range of an L-Lipschitz continuous generative model with bounded k-dimensional inputs. This is mainly motivated by the tremendous success of deep generative models in various real applications. Our reconstruction method is non-iterative (though approximating the projection step may require an iterative procedure) and highly efficient, and it is shown to attain the near-optimal statistical rate of order \sqrt (k \log L)/m , where m is the number of measurements. We consider two specific instances of the SIM, namely noisy 1-bit and cubic measurement models, and perform experiments on image datasets to demonstrate the efficacy of our method. In particular, for the noisy 1-bit measurement model, we show that our non-iterative method significantly outperforms a state-of-the-art iterative method in terms of both accuracy and efficiency. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Non-Iterative_Recovery_From_Nonlinear_Observations_Using_Generative_Models_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Non-Iterative_Recovery_From_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2205.15749 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Non-Iterative_Recovery_From_Nonlinear_Observations_Using_Generative_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Non-Iterative_Recovery_From_Nonlinear_Observations_Using_Generative_Models_CVPR_2022_paper.html | CVPR 2022 | null |
Incremental Cross-View Mutual Distillation for Self-Supervised Medical CT Synthesis | Chaowei Fang, Liang Wang, Dingwen Zhang, Jun Xu, Yixuan Yuan, Junwei Han | Due to the constraints of the imaging device and high cost in operation time, computer tomography (CT) scans are usually acquired with low within-slice resolution. Improving the inter-slice resolution is beneficial to the disease diagnosis for both human experts and computer-aided systems. To this end, this paper builds a novel medical slice synthesis to increase the inter-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy to accomplish this task in the self-supervised learning manner. Specifically, we model this problem from three different views: slice-wise interpolation from axial view and pixel-wise interpolation from coronal and sagittal views. Under this circumstance, the models learned from different views can distill valuable knowledge to guide the learning processes of each other. We can repeat this process to make the models synthesize intermediate slice data with increasing between-slice resolution. To demonstrate the effectiveness of the proposed approach, we conduct comprehensive experiments on a large-scale CT dataset. Quantitative and qualitative comparison results show that our method outperforms state-of-the-art algorithms by clear margins. | https://openaccess.thecvf.com/content/CVPR2022/papers/Fang_Incremental_Cross-View_Mutual_Distillation_for_Self-Supervised_Medical_CT_Synthesis_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2112.10325 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fang_Incremental_Cross-View_Mutual_Distillation_for_Self-Supervised_Medical_CT_Synthesis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fang_Incremental_Cross-View_Mutual_Distillation_for_Self-Supervised_Medical_CT_Synthesis_CVPR_2022_paper.html | CVPR 2022 | null |
Enhancing Adversarial Training With Second-Order Statistics of Weights | Gaojie Jin, Xinping Yi, Wei Huang, Sven Schewe, Xiaowei Huang | Adversarial training has been shown to be one of the most effective approaches to improve the robustness of deep neural networks. It is formalized as a min-max optimization over model weights and adversarial perturbations, where the weights can be optimized through gradient descent methods like SGD. In this paper, we show that treating model weights as random variables allows for enhancing adversarial training through Second-Order Statistics Optimization (S^2O) with respect to the weights. By relaxing a common (but unrealistic) assumption of previous PAC-Bayesian frameworks that all weights are statistically independent, we derive an improved PAC-Bayesian adversarial generalization bound, which suggests that optimizing second-order statistics of weights can effectively tighten the bound. In addition to this theoretical insight, we conduct an extensive set of experiments, which show that S^2O not only improves the robustness and generalization of the trained neural networks when used in isolation, but also integrates easily in state-of-the-art adversarial training techniques like TRADES, AWP, MART, and AVMixup, leading to a measurable improvement of these techniques. The code is available at https://github.com/Alexkael/S2O. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jin_Enhancing_Adversarial_Training_With_Second-Order_Statistics_of_Weights_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jin_Enhancing_Adversarial_Training_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.06020 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jin_Enhancing_Adversarial_Training_With_Second-Order_Statistics_of_Weights_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jin_Enhancing_Adversarial_Training_With_Second-Order_Statistics_of_Weights_CVPR_2022_paper.html | CVPR 2022 | null |
Partially Does It: Towards Scene-Level FG-SBIR With Partial Input | Pinaki Nath Chowdhury, Ayan Kumar Bhunia, Viswanatha Reddy Gajjala, Aneeshan Sain, Tao Xiang, Yi-Zhe Song | We scrutinise an important observation plaguing scene-level sketch research -- that a significant portion of scene sketches are "partial". A quick pilot study reveals: (i) a scene sketch does not necessarily contain all objects in the corresponding photo, due to the subjective holistic interpretation of scenes, (ii) there exists significant empty (white) regions as a result of object-level abstraction, and as a result, (iii) existing scene-level fine-grained sketch-based image retrieval methods collapse as scene sketches become more partial. To solve this "partial" problem, we advocate for a simple set-based approach using optimal transport (OT) to model cross-modal region associativity in a partially-aware fashion. Importantly, we improve upon OT to further account for holistic partialness by comparing intra-modal adjacency matrices. Our proposed method is not only robust to partial scene-sketches but also yields state-of-the-art performance on existing datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chowdhury_Partially_Does_It_Towards_Scene-Level_FG-SBIR_With_Partial_Input_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chowdhury_Partially_Does_It_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14804 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chowdhury_Partially_Does_It_Towards_Scene-Level_FG-SBIR_With_Partial_Input_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chowdhury_Partially_Does_It_Towards_Scene-Level_FG-SBIR_With_Partial_Input_CVPR_2022_paper.html | CVPR 2022 | null |
Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCo | Chaoning Zhang, Kang Zhang, Trung X. Pham, Axi Niu, Zhinan Qiao, Chang D. Yoo, In So Kweon | Contrastive learning (CL) is widely known to require many negative samples, 65536 in MoCo for instance, for which the performance of a dictionary-free framework is often inferior because the negative sample size (NSS) is limited by its mini-batch size (MBS). To decouple the NSS from the MBS, a dynamic dictionary has been adopted in a large volume of CL frameworks, among which arguably the most popular one is MoCo family. In essence, MoCo adopts a momentum-based queue dictionary, for which we perform a fine-grained analysis of its size and consistency. We point out that InfoNCE loss used in MoCo implicitly attract anchors to their corresponding positive sample with various strength of penalties and identify such inter-anchor hardness-awareness property as a major reason for the necessity of a large dictionary. Our findings motivate us to simplify MoCo v2 via the removal of its dictionary as well as momentum. Based on an InfoNCE with the proposed dual temperature, our simplified frameworks, SimMoCo and SimCo, outperform MoCo v2 by a visible margin. Moreover, our work bridges the gap between CL and non-CL frameworks, contributing to a more unified understanding of these two mainstream frameworks in SSL. Code is available at: https://bit.ly/3LkQbaT. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Dual_Temperature_Helps_Contrastive_Learning_Without_Many_Negative_Samples_Towards_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Dual_Temperature_Helps_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.17248 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Dual_Temperature_Helps_Contrastive_Learning_Without_Many_Negative_Samples_Towards_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Dual_Temperature_Helps_Contrastive_Learning_Without_Many_Negative_Samples_Towards_CVPR_2022_paper.html | CVPR 2022 | null |
Moving Window Regression: A Novel Approach to Ordinal Regression | Nyeong-Ho Shin, Seon-Ho Lee, Chang-Su Kim | A novel ordinal regression algorithm, called moving window regression (MWR), is proposed in this paper. First, we propose the notion of relative rank (rho-rank), which is a new order representation scheme for input and reference instances. Second, we develop global and local relative regressors (rho-regressors) to predict rho-ranks within entire and specific rank ranges, respectively. Third, we refine an initial rank estimate iteratively by selecting two reference instances to form a search window and then estimating the rho-rank within the window. Extensive experiments results show that the proposed algorithm achieves the state-of-the-art performances on various benchmark datasets for facial age estimation and historical color image classification. The codes are available at https://github.com/nhshin-mcl/MWR. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shin_Moving_Window_Regression_A_Novel_Approach_to_Ordinal_Regression_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shin_Moving_Window_Regression_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.13122 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shin_Moving_Window_Regression_A_Novel_Approach_to_Ordinal_Regression_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shin_Moving_Window_Regression_A_Novel_Approach_to_Ordinal_Regression_CVPR_2022_paper.html | CVPR 2022 | null |
UniCoRN: A Unified Conditional Image Repainting Network | Jimeng Sun, Shuchen Weng, Zheng Chang, Si Li, Boxin Shi | Conditional image repainting (CIR) is an advanced image editing task, which requires the model to generate visual content in user-specified regions conditioned on multiple cross-modality constraints, and composite the visual content with the provided background seamlessly. Existing methods based on two-phase architecture design assume dependency between phases and cause color-image incongruity. To solve these problems, we propose a novel Unified Conditional image Repainting Network (UniCoRN). We break the two-phase assumption in CIR task by constructing the interaction and dependency relationship between background and other conditions. We further introduce the hierarchical structure into cross-modality similarity model to capture feature patterns at different levels and bridge the gap between visual content and color condition. A new LANDSCAPE-CIR dataset is collected and annotated to expand the application scenarios of the CIR task. Experiments show that UniCoRN achieves higher synthetic quality, better condition consistency, and more realistic compositing effect. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_UniCoRN_A_Unified_Conditional_Image_Repainting_Network_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_UniCoRN_A_Unified_Conditional_Image_Repainting_Network_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_UniCoRN_A_Unified_Conditional_Image_Repainting_Network_CVPR_2022_paper.html | CVPR 2022 | null |
Forecasting Characteristic 3D Poses of Human Actions | Christian Diller, Thomas Funkhouser, Angela Dai | We propose the task of forecasting characteristic 3d poses: from a short sequence observation of a person, predict a future 3d pose of that person in a likely action-defining, characteristic pose - for instance, from observing a person picking up an apple, predict the pose of the person eating the apple. Prior work on human motion prediction estimates future poses at fixed time intervals. Although easy to define, this frame-by-frame formulation confounds temporal and intentional aspects of human action. Instead, we define a semantically meaningful pose prediction task that decouples the predicted pose from time, taking inspiration from goal-directed behavior. To predict characteristic poses, we propose a probabilistic approach that models the possible multi-modality in the distribution of likely characteristic poses. We then sample future pose hypotheses from the predicted distribution in an autoregressive fashion to model dependencies between joints. To evaluate our method, we construct a dataset of manually annotated characteristic 3d poses. Our experiments with this dataset suggest that our proposed probabilistic approach outperforms state-of-the-art methods by 26% on average. | https://openaccess.thecvf.com/content/CVPR2022/papers/Diller_Forecasting_Characteristic_3D_Poses_of_Human_Actions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Diller_Forecasting_Characteristic_3D_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2011.15079 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Diller_Forecasting_Characteristic_3D_Poses_of_Human_Actions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Diller_Forecasting_Characteristic_3D_Poses_of_Human_Actions_CVPR_2022_paper.html | CVPR 2022 | null |
ACPL: Anti-Curriculum Pseudo-Labelling for Semi-Supervised Medical Image Classification | Fengbei Liu, Yu Tian, Yuanhong Chen, Yuyuan Liu, Vasileios Belagiannis, Gustavo Carneiro | Effective semi-supervised learning (SSL) in medical image analysis (MIA) must address two challenges: 1) work effectively on both multi-class (e.g., lesion classification) and multi-label (e.g., multiple-disease diagnosis) problems, and 2) handle imbalanced learning (because of the high variance in disease prevalence). One strategy to explore in SSL MIA is based on the pseudo labelling strategy, but it has a few shortcomings. Pseudo-labelling has in general lower accuracy than consistency learning, it is not specifically designed for both multi-class and multi-label problems, and it can be challenged by imbalanced learning. In this paper, unlike traditional methods that select confident pseudo label by threshold, we propose a new SSL algorithm, called anti-curriculum pseudo-labelling (ACPL), which introduces novel techniques to select informative unlabelled samples, improving training balance and allowing the model to work for both multi-label and multi-class problems, and to estimate pseudo labels by an accurate ensemble of classifiers (improving pseudo label accuracy). We run extensive experiments to evaluate ACPL on two public medical image classification benchmarks: Chest X-Ray14 for thorax disease multi-label classification and ISIC2018 for skin lesion multi-class classification. Our method outperforms previous SOTA SSL methods on both datasets | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_ACPL_Anti-Curriculum_Pseudo-Labelling_for_Semi-Supervised_Medical_Image_Classification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_ACPL_Anti-Curriculum_Pseudo-Labelling_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.12918 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_ACPL_Anti-Curriculum_Pseudo-Labelling_for_Semi-Supervised_Medical_Image_Classification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_ACPL_Anti-Curriculum_Pseudo-Labelling_for_Semi-Supervised_Medical_Image_Classification_CVPR_2022_paper.html | CVPR 2022 | null |
Learning to Deblur Using Light Field Generated and Real Defocus Images | Lingyan Ruan, Bin Chen, Jizhou Li, Miuling Lam | Defocus deblurring is a challenging task due to the spatially varying nature of defocus blur. While deep learning approach shows great promise in solving image restoration problems, defocus deblurring demands accurate training data that consists of all-in-focus and defocus image pairs, which is difficult to collect. Naive two-shot capturing cannot achieve pixel-wise correspondence between the defocused and all-in-focus image pairs. Synthetic aperture of light fields is suggested to be a more reliable way to generate accurate image pairs. However, the defocus blur generated from light field data is different from that of the images captured with a traditional digital camera. In this paper, we propose a novel deep defocus deblurring network that leverages the strength and overcomes the shortcoming of light fields. We first train the network on a light field-generated dataset for its highly accurate image correspondence. Then, we fine-tune the network using feature loss on another dataset collected by the two-shot method to alleviate the differences between the defocus blur exists in the two domains. This strategy is proved to be highly effective and able to achieve the state-of-the-art performance both quantitatively and qualitatively on multiple test sets. Extensive ablation studies have been conducted to analyze the effect of each network module to the final performance. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ruan_Learning_to_Deblur_Using_Light_Field_Generated_and_Real_Defocus_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ruan_Learning_to_Deblur_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.00367 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ruan_Learning_to_Deblur_Using_Light_Field_Generated_and_Real_Defocus_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ruan_Learning_to_Deblur_Using_Light_Field_Generated_and_Real_Defocus_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection | Nicolae-Cătălin Ristea, Neelu Madan, Radu Tudor Ionescu, Kamal Nasrollahi, Fahad Shahbaz Khan, Thomas B. Moeslund, Mubarak Shah | Anomaly detection is commonly pursued as a one-class classification problem, where models can only learn from normal training samples, while being evaluated on both normal and abnormal test samples. Among the successful approaches for anomaly detection, a distinguished category of methods relies on predicting masked information (e.g. patches, future frames, etc.) and leveraging the reconstruction error with respect to the masked information as an abnormality score. Different from related methods, we propose to integrate the reconstruction-based functionality into a novel self-supervised predictive architectural building block. The proposed self-supervised block is generic and can easily be incorporated into various state-of-the-art anomaly detection methods. Our block starts with a convolutional layer with dilated filters, where the center area of the receptive field is masked. The resulting activation maps are passed through a channel attention module. Our block is equipped with a loss that minimizes the reconstruction error with respect to the masked area in the receptive field. We demonstrate the generality of our block by integrating it into several state-of-the-art frameworks for anomaly detection on image and video, providing empirical evidence that shows considerable performance improvements on MVTec AD, Avenue, and ShanghaiTech. We release our code as open source at: https://github.com/ristea/sspcab. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ristea_Self-Supervised_Predictive_Convolutional_Attentive_Block_for_Anomaly_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ristea_Self-Supervised_Predictive_Convolutional_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.09099 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ristea_Self-Supervised_Predictive_Convolutional_Attentive_Block_for_Anomaly_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ristea_Self-Supervised_Predictive_Convolutional_Attentive_Block_for_Anomaly_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Safe Self-Refinement for Transformer-Based Domain Adaptation | Tao Sun, Cheng Lu, Tianshuo Zhang, Haibin Ling | Unsupervised Domain Adaptation (UDA) aims to leverage a label-rich source domain to solve tasks on a related unlabeled target domain. It is a challenging problem especially when a large domain gap lies between the source and target domains. In this paper we propose a novel solution named SSRT (Safe Self-Refinement for Transformer-based domain adaptation), which brings improvement from two aspects. First, encouraged by the success of vision transformers in various vision tasks, we arm SSRT with a transformer backbone. We find that the combination of vision transformer with simple adversarial adaptation surpasses best reported Convolutional Neural Network (CNN)-based results on the challenging DomainNet benchmark, showing its strong transferable feature representation. Second, to reduce the risk of model collapse and improve the effectiveness of knowledge transfer between domains with large gaps, we propose a Safe Self-Refinement strategy. Specifically, SSRT utilizes predictions of perturbed target domain data to refine the model. Since the model capacity of vision transformer is large and predictions in such challenging tasks can be noisy, a safe training mechanism is designed to adaptively adjust learning configuration. Extensive evaluations are conducted on several widely tested UDA benchmarks and SSRT achieves consistently the best performances, including 85.43% on Office-Home, 88.76% on VisDA-2017 and 45.2% on DomainNet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_Safe_Self-Refinement_for_Transformer-Based_Domain_Adaptation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.07683 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Safe_Self-Refinement_for_Transformer-Based_Domain_Adaptation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Safe_Self-Refinement_for_Transformer-Based_Domain_Adaptation_CVPR_2022_paper.html | CVPR 2022 | null |
Density-Preserving Deep Point Cloud Compression | Yun He, Xinlin Ren, Danhang Tang, Yinda Zhang, Xiangyang Xue, Yanwei Fu | Local density of point clouds is crucial for representing local details, but has been overlooked by existing point cloud compression methods. To address this, we propose a novel deep point cloud compression method that preserves local density information. Our method works in an auto-encoder fashion: the encoder downsamples the points and learns point-wise features, while the decoder upsamples the points using these features. Specifically, we propose to encode local geometry and density with three embeddings: density embedding, local position embedding and ancestor embedding. During the decoding, we explicitly predict the upsampling factor for each point, and the directions and scales of the upsampled points. To mitigate the clustered points issue in existing methods, we design a novel sub-point convolution layer, and an upsampling block with adaptive scale. Furthermore, our method can also compress point-wise attributes, such as normal. Extensive qualitative and quantitative results on SemanticKITTI and ShapeNet demonstrate that our method achieves the state-of-the-art rate-distortion trade-off. | https://openaccess.thecvf.com/content/CVPR2022/papers/He_Density-Preserving_Deep_Point_Cloud_Compression_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/He_Density-Preserving_Deep_Point_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.12684 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/He_Density-Preserving_Deep_Point_Cloud_Compression_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/He_Density-Preserving_Deep_Point_Cloud_Compression_CVPR_2022_paper.html | CVPR 2022 | null |
StyleMesh: Style Transfer for Indoor 3D Scene Reconstructions | Lukas Höllein, Justin Johnson, Matthias Nießner | We apply style transfer on mesh reconstructions of indoor scenes. This enables VR applications like experiencing 3D environments painted in the style of a favorite artist. Style transfer typically operates on 2D images, making stylization of a mesh challenging. When optimized over a variety of poses, stylization patterns become stretched out and inconsistent in size. On the other hand, model-based 3D style transfer methods exist that allow stylization from a sparse set of images, but they require a network at inference time. To this end, we optimize an explicit texture for the reconstructed mesh of a scene and stylize it jointly from all available input images. Our depth- and angle-aware optimization leverages surface normal and depth data of the underlying mesh to create a uniform and consistent stylization for the whole scene. Our experiments show that our method creates sharp and detailed results for the complete scene without view-dependent artifacts. Through extensive ablation studies, we show that the proposed 3D awareness enables style transfer to be applied to the 3D domain of a mesh. Our method can be used to render a stylized mesh in real-time with traditional rendering pipelines. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hollein_StyleMesh_Style_Transfer_for_Indoor_3D_Scene_Reconstructions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hollein_StyleMesh_Style_Transfer_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hollein_StyleMesh_Style_Transfer_for_Indoor_3D_Scene_Reconstructions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hollein_StyleMesh_Style_Transfer_for_Indoor_3D_Scene_Reconstructions_CVPR_2022_paper.html | CVPR 2022 | null |
Which Model To Transfer? Finding the Needle in the Growing Haystack | Cedric Renggli, André Susano Pinto, Luka Rimanic, Joan Puigcerver, Carlos Riquelme, Ce Zhang, Mario Lučić | Transfer learning has been recently popularized as a data-efficient alternative to training models from scratch, in particular for computer vision tasks where it provides a remarkably solid baseline. The emergence of rich model repositories, such as TensorFlow Hub, enables the practitioners and researchers to unleash the potential of these models across a wide range of downstream tasks. As these repositories keep growing exponentially, efficiently selecting a good model for the task at hand becomes paramount. We provide a formalization of this problem through a familiar notion of regret and introduce the predominant strategies, namely task-agnostic (e.g. ranking models by their ImageNet performance) and task-aware search strategies (such as linear or kNN evaluation). We conduct a large-scale empirical study and show that both task-agnostic and task-aware methods can yield high regret. We then propose a simple and computationally efficient hybrid search strategy which outperforms the existing approaches. We highlight the practical benefits of the proposed solution on a set of 19 diverse vision tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Renggli_Which_Model_To_Transfer_Finding_the_Needle_in_the_Growing_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Renggli_Which_Model_To_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2010.06402 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Renggli_Which_Model_To_Transfer_Finding_the_Needle_in_the_Growing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Renggli_Which_Model_To_Transfer_Finding_the_Needle_in_the_Growing_CVPR_2022_paper.html | CVPR 2022 | null |
Fast and Unsupervised Action Boundary Detection for Action Segmentation | Zexing Du, Xue Wang, Guoqing Zhou, Qing Wang | To deal with the great number of untrimmed videos produced every day, we propose an efficient unsupervised action segmentation method by detecting boundaries, named action boundary detection (ABD). In particular, the proposed method has the following advantages: no training stage and low-latency inference. To detect action boundaries, we estimate the similarities across smoothed frames, which inherently have the properties of internal consistency within actions and external discrepancy across actions. Under this circumstance, we successfully transfer the boundary detection task into the change point detection based on the similarity. Then, non-maximum suppression (NMS) is conducted in local windows to select the smallest points as candidate boundaries. In addition, a clustering algorithm is followed to refine the initial proposals. Moreover, we also extend ABD to the online setting, which enables real-time action segmentation in long untrimmed videos. By evaluating on four challenging datasets, our method achieves state-of-the-art performance. Moreover, thanks to the efficiency of ABD, we achieve the best trade-off between the accuracy and the inference time compared with existing unsupervised approaches. | https://openaccess.thecvf.com/content/CVPR2022/papers/Du_Fast_and_Unsupervised_Action_Boundary_Detection_for_Action_Segmentation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Du_Fast_and_Unsupervised_Action_Boundary_Detection_for_Action_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Du_Fast_and_Unsupervised_Action_Boundary_Detection_for_Action_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Class-Incremental Learning With Strong Pre-Trained Models | Tz-Ying Wu, Gurumurthy Swaminathan, Zhizhong Li, Avinash Ravichandran, Nuno Vasconcelos, Rahul Bhotika, Stefano Soatto | Class-incremental learning (CIL) has been widely studied under the setting of starting from a small number of classes (base classes). Instead, we explore an understudied real-world setting of CIL that starts with a strong model pre-trained on a large number of base classes. We hypothesize that a strong base model can provide a good representation for novel classes and incremental learning can be done with small adaptations. We propose a 2-stage training scheme, i) feature augmentation - cloning part of the backbone and fine-tuning it on the novel data, and ii) fusion - combining the base and novel classifiers into a unified classifier. Experiments show that the proposed method significantly outperforms state-of-the-art CIL methods on the large-scale ImageNet dataset (e.g. +10% overall accuracy than the best). We also propose and analyze understudied practical CIL scenarios, such as base-novel overlap with distribution shift. Our proposed method is robust and generalizes to all analyzed CIL settings. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Class-Incremental_Learning_With_Strong_Pre-Trained_Models_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_Class-Incremental_Learning_With_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.03634 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Class-Incremental_Learning_With_Strong_Pre-Trained_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Class-Incremental_Learning_With_Strong_Pre-Trained_Models_CVPR_2022_paper.html | CVPR 2022 | null |
Robust Optimization As Data Augmentation for Large-Scale Graphs | Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein | Data augmentation helps neural networks generalize better by enlarging the training set, but it remains an open question how to effectively augment graph data to enhance the performance of GNNs (Graph Neural Networks). While most existing graph regularizers focus on manipulating graph topological structures by adding/removing edges, we offer a method to augment node features for better performance. We propose FLAG (Free Large-scale Adversarial Augmentation on Graphs), which iteratively augments node features with gradient-based adversarial perturbations during training. By making the model invariant to small fluctuations in input data, our method helps models generalize to out-of-distribution samples and boosts model performance at test time. FLAG is a general-purpose approach for graph data, which universally works in node classification, link prediction, and graph classification tasks. FLAG is also highly flexible and scalable, and is deployable with arbitrary GNN backbones and large-scale datasets. We demonstrate the efficacy and stability of our method through extensive experiments and ablation studies. We also provide intuitive observations for a deeper understanding of our method. We open source our implementation at https://github.com/devnkong/FLAG. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kong_Robust_Optimization_As_Data_Augmentation_for_Large-Scale_Graphs_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kong_Robust_Optimization_As_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2010.09891 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kong_Robust_Optimization_As_Data_Augmentation_for_Large-Scale_Graphs_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kong_Robust_Optimization_As_Data_Augmentation_for_Large-Scale_Graphs_CVPR_2022_paper.html | CVPR 2022 | null |
Robust Structured Declarative Classifiers for 3D Point Clouds: Defending Adversarial Attacks With Implicit Gradients | Kaidong Li, Ziming Zhang, Cuncong Zhong, Guanghui Wang | Deep neural networks for 3D point cloud classification, such as PointNet, have been demonstrated to be vulnerable to adversarial attacks. Current adversarial defenders often learn to denoise the (attacked) point clouds by reconstruction, and then feed them to the classifiers as input. In contrast to the literature, we propose a family of robust structured declarative classifiers for point cloud classification, where the internal constrained optimization mechanism can effectively defend adversarial attacks through implicit gradients. Such classifiers can be formulated using a bilevel optimization framework. We further propose an effective and efficient instantiation of our approach, namely, Lattice Point Classifier (LPC), based on structured sparse coding in the permutohedral lattice and 2D convolutional neural networks (CNNs) that is end-to-end trainable. We demonstrate state-of-the-art robust point cloud classification performance on ModelNet40 and ScanNet under seven different attackers. For instance, we achieve 89.51% and 83.16% test accuracy on each dataset under the recent JGBA attacker that outperforms DUP-Net and IF-Defense with PointNet by 70%. Demo code is available at https://zhang-vislab.github.io. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Robust_Structured_Declarative_Classifiers_for_3D_Point_Clouds_Defending_Adversarial_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.15245 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Robust_Structured_Declarative_Classifiers_for_3D_Point_Clouds_Defending_Adversarial_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Robust_Structured_Declarative_Classifiers_for_3D_Point_Clouds_Defending_Adversarial_CVPR_2022_paper.html | CVPR 2022 | null |
PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes | Yu-Ying Yeh, Zhengqin Li, Yannick Hold-Geoffroy, Rui Zhu, Zexiang Xu, Miloš Hašan, Kalyan Sunkavalli, Manmohan Chandraker | Most indoor 3D scene reconstruction methods focus on recovering 3D geometry and scene layout. In this work, we go beyond this to propose PhotoScene, a framework that takes input image(s) of a scene along with approximately aligned CAD geometry (either reconstructed automatically or manually specified) and builds a photorealistic digital twin with high-quality materials and similar lighting. We model scene materials using procedural material graphs; such graphs represent photorealistic and resolution-independent materials. We optimize the parameters of these graphs and their texture scale and rotation, as well as the scene lighting to best match the input image via a differentiable rendering layer. We evaluate our technique on objects and layout reconstructions from ScanNet, SUN RGB-D and stock photographs, and demonstrate that our method reconstructs high-quality, fully relightable 3D scenes that can be re-rendered under arbitrary viewpoints, zooms and lighting. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yeh_PhotoScene_Photorealistic_Material_and_Lighting_Transfer_for_Indoor_Scenes_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yeh_PhotoScene_Photorealistic_Material_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yeh_PhotoScene_Photorealistic_Material_and_Lighting_Transfer_for_Indoor_Scenes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yeh_PhotoScene_Photorealistic_Material_and_Lighting_Transfer_for_Indoor_Scenes_CVPR_2022_paper.html | CVPR 2022 | null |
Improving the Transferability of Targeted Adversarial Examples Through Object-Based Diverse Input | Junyoung Byun, Seungju Cho, Myung-Joon Kwon, Hee-Seon Kim, Changick Kim | The transferability of adversarial examples allows the deception on black-box models, and transfer-based targeted attacks have attracted a lot of interest due to their practical applicability. To maximize the transfer success rate, adversarial examples should avoid overfitting to the source model, and image augmentation is one of the primary approaches for this. However, prior works utilize simple image transformations such as resizing, which limits input diversity. To tackle this limitation, we propose the object-based diverse input (ODI) method that draws an adversarial image on a 3D object and induces the rendered image to be classified as the target class. Our motivation comes from the humans' superior perception of an image printed on a 3D object. If the image is clear enough, humans can recognize the image content in a variety of viewing conditions. Likewise, if an adversarial example looks like the target class to the model, the model should also classify the rendered image of the 3D object as the target class. The ODI method effectively diversifies the input by leveraging an ensemble of multiple source objects and randomizing viewing conditions. In our experimental results on the ImageNet-Compatible dataset, this method boosts the average targeted attack success rate from 28.3% to 47.0% compared to the state-of-the-art methods. We also demonstrate the applicability of the ODI method to adversarial examples on the face verification task and its superior performance improvement. Our code is available at https://github.com/dreamflake/ODI. | https://openaccess.thecvf.com/content/CVPR2022/papers/Byun_Improving_the_Transferability_of_Targeted_Adversarial_Examples_Through_Object-Based_Diverse_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Byun_Improving_the_Transferability_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09123 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Byun_Improving_the_Transferability_of_Targeted_Adversarial_Examples_Through_Object-Based_Diverse_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Byun_Improving_the_Transferability_of_Targeted_Adversarial_Examples_Through_Object-Based_Diverse_CVPR_2022_paper.html | CVPR 2022 | null |
IRON: Inverse Rendering by Optimizing Neural SDFs and Materials From Photometric Images | Kai Zhang, Fujun Luan, Zhengqi Li, Noah Snavely | We propose a neural inverse rendering pipeline called IRON that operates on photometric images and outputs high-quality 3D content in the format of triangle meshes and material textures readily deployable in existing graphics pipelines. We propose a neural inverse rendering pipeline called IRON that operates on photometric images and outputs high-quality 3D content in the format of triangle meshes and material textures readily deployable in existing graphics pipelines. Our method adopts neural representations for geometry as signed distance fields (SDFs) and materials during optimization to enjoy their flexibility and compactness, and features a hybrid optimization scheme for neural SDFs: first, optimize using a volumetric radiance field approach to recover correct topology, then optimize further using edge-aware physics-based surface rendering for geometry refinement and disentanglement of materials and lighting. In the second stage, we also draw inspiration from mesh-based differentiable rendering, and design a novel edge sampling algorithm for neural SDFs to further improve performance. We show that our IRON achieves significantly better inverse rendering quality compared to prior works. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_IRON_Inverse_Rendering_by_Optimizing_Neural_SDFs_and_Materials_From_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_IRON_Inverse_Rendering_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.02232 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_IRON_Inverse_Rendering_by_Optimizing_Neural_SDFs_and_Materials_From_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_IRON_Inverse_Rendering_by_Optimizing_Neural_SDFs_and_Materials_From_CVPR_2022_paper.html | CVPR 2022 | null |
ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfer | Ruohan Gao, Zilin Si, Yen-Yu Chang, Samuel Clarke, Jeannette Bohg, Li Fei-Fei, Wenzhen Yuan, Jiajun Wu | Objects play a crucial role in our everyday activities. Though multisensory object-centric learning has shown great potential lately, the modeling of objects in prior work is rather unrealistic. ObjectFolder 1.0 is a recent dataset that introduces 100 virtualized objects with visual, auditory, and tactile sensory data. However, the dataset is small in scale and the multisensory data is of limited quality, hampering generalization to real-world scenarios. We present ObjectFolder 2.0, a large-scale, multisensory dataset of common household objects in the form of implicit neural representations that significantly enhances ObjectFolder 1.0 in three aspects. First, our dataset is 10 times larger in the amount of objects and orders of magnitude faster in rendering time. Second, we significantly improve the multisensory rendering quality for all three modalities. Third, we show that models learned from virtual objects in our dataset successfully transfer to their real-world counterparts in three challenging tasks: object scale estimation, contact localization, and shape reconstruction. ObjectFolder 2.0 offers a new path and testbed for multisensory learning in computer vision and robotics. The dataset is available at https://github.com/rhgao/ObjectFolder. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gao_ObjectFolder_2.0_A_Multisensory_Object_Dataset_for_Sim2Real_Transfer_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gao_ObjectFolder_2.0_A_Multisensory_Object_Dataset_for_Sim2Real_Transfer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gao_ObjectFolder_2.0_A_Multisensory_Object_Dataset_for_Sim2Real_Transfer_CVPR_2022_paper.html | CVPR 2022 | null |
Versatile Multi-Modal Pre-Training for Human-Centric Perception | Fangzhou Hong, Liang Pan, Zhongang Cai, Ziwei Liu | Human-centric perception plays a vital role in vision and graphics. But their data annotations are prohibitively expensive. Therefore, it is desirable to have a versatile pre-train model that serves as a foundation for data-efficient downstream tasks transfer. To this end, we propose the Human-Centric Multi-Modal Contrastive Learning framework HCMoCo that leverages the multi-modal nature of human data (e.g. RGB, depth, 2D keypoints) for effective representation learning. The objective comes with two main challenges: dense pre-train for multi-modality data, efficient usage of sparse human priors. To tackle the challenges, we design the novel Dense Intra-sample Contrastive Learning and Sparse Structure-aware Contrastive Learning targets by hierarchically learning a modal-invariant latent space featured with continuous and ordinal feature distribution and structure-aware semantic consistency. HCMoCo provides pre-train for different modalities by combining heterogeneous datasets, which allows efficient usage of existing task-specific human data. Extensive experiments on four downstream tasks of different modalities demonstrate the effectiveness of HCMoCo, especially under data-efficient settings (7.16% and 12% improvement on DensePose Estimation and Human Parsing). Moreover, we demonstrate the versatility of HCMoCo by exploring cross-modality supervision and missing-modality inference, validating its strong ability in cross-modal association and reasoning. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hong_Versatile_Multi-Modal_Pre-Training_for_Human-Centric_Perception_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.13815 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hong_Versatile_Multi-Modal_Pre-Training_for_Human-Centric_Perception_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hong_Versatile_Multi-Modal_Pre-Training_for_Human-Centric_Perception_CVPR_2022_paper.html | CVPR 2022 | null |
360MonoDepth: High-Resolution 360deg Monocular Depth Estimation | Manuel Rey-Area, Mingze Yuan, Christian Richardt | 360deg cameras can capture complete environments in a single shot, which makes 360deg imagery alluring in many computer vision tasks. However, monocular depth estimation remains a challenge for 360deg data, particularly for high resolutions like 2K (2048x1024) and beyond that are important for novel-view synthesis and virtual reality applications. Current CNN-based methods do not support such high resolutions due to limited GPU memory. In this work, we propose a flexible framework for monocular depth estimation from high-resolution 360deg images using tangent images. We project the 360deg input image onto a set of tangent planes that produce perspective views, which are suitable for the latest, most accurate state-of-the-art perspective monocular depth estimators. To achieve globally consistent disparity estimates, we recombine the individual depth estimates using deformable multi-scale alignment followed by gradient-domain blending. The result is a dense, high-resolution 360deg depth map with a high level of detail, also for outdoor scenes which are not supported by existing methods. Our source code and data are available at https://manurare.github.io/360monodepth/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Rey-Area_360MonoDepth_High-Resolution_360deg_Monocular_Depth_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Rey-Area_360MonoDepth_High-Resolution_360deg_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Rey-Area_360MonoDepth_High-Resolution_360deg_Monocular_Depth_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Rey-Area_360MonoDepth_High-Resolution_360deg_Monocular_Depth_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
Splicing ViT Features for Semantic Appearance Transfer | Narek Tumanyan, Omer Bar-Tal, Shai Bagon, Tali Dekel | We present a method for semantically transferring the visual appearance of one natural image to another. Specifically, our goal is to generate an image in which objects in a source structure image are "painted" with the visual appearance of their semantically related objects in a target appearance image. Our method works by training a generator given only a single structure/appearance image pair as input. To integrate semantic information into our framework---a pivotal component in tackling this task---our key idea is to leverage a pre-trained and fixed Vision Transformer (ViT) model which serves as an external semantic prior. Specifically, we derive novel representations of structure and appearance extracted from deep ViT features, untwisting them from the learned self-attention modules. We then establish an objective function that splices the desired structure and appearance representations, interweaving them together in the space of ViT features. Our framework, which we term "Splice", does not involve adversarial training, nor does it require any additional input information such as semantic segmentation or correspondences, and can generate high resolution results, e.g., work in HD. We demonstrate high quality results on a variety of in-the-wild image pairs, under significant variations in the number of objects, their pose and appearance. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tumanyan_Splicing_ViT_Features_for_Semantic_Appearance_Transfer_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tumanyan_Splicing_ViT_Features_for_Semantic_Appearance_Transfer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tumanyan_Splicing_ViT_Features_for_Semantic_Appearance_Transfer_CVPR_2022_paper.html | CVPR 2022 | null |
Contrastive Regression for Domain Adaptation on Gaze Estimation | Yaoming Wang, Yangzhou Jiang, Jin Li, Bingbing Ni, Wenrui Dai, Chenglin Li, Hongkai Xiong, Teng Li | Appearance-based Gaze Estimation leverages deep neural networks to regress the gaze direction from monocular images and achieve impressive performance. However, its success depends on expensive and cumbersome annotation capture. When lacking precise annotation, the large domain gap hinders the performance of trained models on new domains. In this paper, we propose a novel gaze adaptation approach, namely Contrastive Regression Gaze Adaptation (CRGA), for generalizing gaze estimation on the target domain in an unsupervised manner. CRGA leverages the Contrastive Domain Generalization (CDG) module to learn the stable representation from the source domain and leverages the Contrastive Self-training Adaptation (CSA) module to learn from the pseudo labels on the target domain. The core of both CDG and CSA is the Contrastive Regression (CR) loss, a novel contrastive loss for regression by pulling features with closer gaze directions closer together while pushing features with farther gaze directions farther apart. Experimentally, we choose ETH-XGAZE and Gaze-360 as the source domain and test the domain generalization and adaptation performance on MPIIGAZE, RT-GENE, GazeCapture, EyeDiap respectively. The results demonstrate that our CRGA achieves remarkable performance improvement compared with the baseline models and also outperforms the state-of-the-art domain adaptation approaches on gaze adaptation tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Contrastive_Regression_for_Domain_Adaptation_on_Gaze_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Contrastive_Regression_for_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Contrastive_Regression_for_Domain_Adaptation_on_Gaze_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Contrastive_Regression_for_Domain_Adaptation_on_Gaze_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory Prediction | Mihee Lee, Samuel S. Sohn, Seonghyeon Moon, Sejong Yoon, Mubbasir Kapadia, Vladimir Pavlovic | Accurate long-term trajectory prediction in complex scenes, where multiple agents (e.g., pedestrians or vehicles) interact with each other and the environment while attempting to accomplish diverse and often unknown goals, is a challenging stochastic forecasting problem. In this work, we propose MUSE-VAE, a new probabilistic modeling framework based on a cascade of Conditional VAEs, which tackles the long-term, uncertain trajectory prediction task using a coarse-to-fine multi-factor forecasting architecture. In its Macro stage, the model learns a joint pixel-space representation of two key factors, the underlying environment and the agent movements, to predict the long and short term motion goals. Conditioned on them, the Micro stage learns a fine-grained spatio-temporal representation for the prediction of individual agent trajectories. The VAE backbones across the two stages make it possible to naturally account for the joint uncertainty at both levels of granularity. As a result, MUSE-VAE offers diverse and simultaneously more accurate predictions compared to the current state-of-the-art. We demonstrate these assertions through a comprehensive set of experiments on nuScenes and SDD benchmarks as well as PFSD, a new synthetic dataset, which challenges the forecasting ability of models on complex agent-environment interaction scenarios. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_MUSE-VAE_Multi-Scale_VAE_for_Environment-Aware_Long_Term_Trajectory_Prediction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lee_MUSE-VAE_Multi-Scale_VAE_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_MUSE-VAE_Multi-Scale_VAE_for_Environment-Aware_Long_Term_Trajectory_Prediction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_MUSE-VAE_Multi-Scale_VAE_for_Environment-Aware_Long_Term_Trajectory_Prediction_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-View Consistent Generative Adversarial Networks for 3D-Aware Image Synthesis | Xuanmeng Zhang, Zhedong Zheng, Daiheng Gao, Bang Zhang, Pan Pan, Yi Yang | 3D-aware image synthesis aims to generate images of objects from multiple views by learning a 3D representation. However, one key challenge remains: existing approaches lack geometry constraints, hence usually fail to generate multi-view consistent images. To address this challenge, we propose Multi-View Consistent Generative Adversarial Networks (MVCGAN) for high-quality 3D-aware image synthesis with geometry constraints. By leveraging the underlying 3D geometry information of generated images, i.e., depth and camera transformation matrix, we explicitly establish stereo correspondence between views to perform multi-view joint optimization. In particular, we enforce the photometric consistency between pairs of views and integrate a stereo mixup mechanism into the training process, encouraging the model to reason about the correct 3D shape. Besides, we design a two-stage training strategy with feature-level multi-view joint optimization to improve the image quality. Extensive experiments on three datasets demonstrate that MVCGAN achieves the state-of-the-art performance for 3D-aware image synthesis. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Multi-View_Consistent_Generative_Adversarial_Networks_for_3D-Aware_Image_Synthesis_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Multi-View_Consistent_Generative_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.06307 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Multi-View_Consistent_Generative_Adversarial_Networks_for_3D-Aware_Image_Synthesis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Multi-View_Consistent_Generative_Adversarial_Networks_for_3D-Aware_Image_Synthesis_CVPR_2022_paper.html | CVPR 2022 | null |
Putting People in Their Place: Monocular Regression of 3D People in Depth | Yu Sun, Wu Liu, Qian Bao, Yili Fu, Tao Mei, Michael J. Black | Given an image with multiple people, our goal is to directly regress the pose and shape of all the people as well as their relative depth. Inferring the depth of a person in an image, however, is fundamentally ambiguous without knowing their height. This is particularly problematic when the scene contains people of very different sizes, e.g. from infants to adults. To solve this, we need several things. First, we develop a novel method to infer the poses and depth of multiple people in a single image. While previous work that estimates multiple people does so by reasoning in the image plane, our method, called BEV, adds an additional imaginary Bird's-Eye-View representation to explicitly reason about depth. BEV reasons simultaneously about body centers in the image and in depth and, by combing these, estimates 3D body position. Unlike prior work, BEV is a single-shot method that is end-to-end differentiable. Second, height varies with age, making it impossible to resolve depth without also estimating the age of people in the image. To do so, we exploit a 3D body model space that lets BEV infer shapes from infants to adults. Third, to train BEV, we need a new dataset. Specifically, we create a "Relative Human" (RH) dataset that includes age labels and relative depth relationships between the people in the images. Extensive experiments on RH and AGORA demonstrate the effectiveness of the model and training scheme. BEV outperforms existing methods on depth reasoning, child shape estimation, and robustness to occlusion. The code and dataset are released for research purposes. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_Putting_People_in_Their_Place_Monocular_Regression_of_3D_People_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sun_Putting_People_in_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.08274 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Putting_People_in_Their_Place_Monocular_Regression_of_3D_People_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Putting_People_in_Their_Place_Monocular_Regression_of_3D_People_CVPR_2022_paper.html | CVPR 2022 | null |
POCO: Point Convolution for Surface Reconstruction | Alexandre Boulch, Renaud Marlet | Implicit neural networks have been successfully used for surface reconstruction from point clouds. However, many of them face scalability issues as they encode the isosurface function of a whole object or scene into a single latent vector. To overcome this limitation, a few approaches infer latent vectors on a coarse regular 3D grid or on 3D patches, and interpolate them to answer occupancy queries. In doing so, they loose the direct connection with the input points sampled on the surface of objects, and they attach information uniformly in space rather than where it matters the most, i.e., near the surface. Besides, relying on fixed patch sizes may require discretization tuning. To address these issues, we propose to use point cloud convolutions and compute latent vectors at each input point. We then perform a learning-based interpolation on nearest neighbors using inferred weights. Experiments on both object and scene datasets show that our approach significantly outperforms other methods on most classical metrics, producing finer details and better reconstructing thinner volumes. The code is available at https://github.com/valeoai/POCO | https://openaccess.thecvf.com/content/CVPR2022/papers/Boulch_POCO_Point_Convolution_for_Surface_Reconstruction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Boulch_POCO_Point_Convolution_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.01831 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Boulch_POCO_Point_Convolution_for_Surface_Reconstruction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Boulch_POCO_Point_Convolution_for_Surface_Reconstruction_CVPR_2022_paper.html | CVPR 2022 | null |
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