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C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds | Albert Pumarola, Stefan Popov, Francesc Moreno-Noguer, Vittorio Ferrari | Flow-based generative models have highly desirable properties like exact log-likelihood evaluation and exact latent-variable inference, however they are still in their infancy and have not received as much attention as alternative generative models. In this paper, we introduce C-Flow, a novel conditioning scheme that brings normalizing flows to an entirely new scenario with great possibilities for multimodal data modeling. C-Flow is based on a parallel sequence of invertible mappings in which a source flow guides the target flow at every step, enabling fine-grained control over the generation process. We also devise a new strategy to model unordered 3D point clouds that, in combination with the conditioning scheme, makes it possible to address 3D reconstruction from a single image and its inverse problem of rendering an image given a point cloud. We demonstrate our conditioning method to be very adaptable, being also applicable to image manipulation, style transfer and multi-modal image-to-image mapping in a diversity of domains, including RGB images, segmentation maps and edge masks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Pumarola_C-Flow_Conditional_Generative_Flow_Models_for_Images_and_3D_Point_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Pumarola_C-Flow_Conditional_Generative_Flow_Models_for_Images_and_3D_Point_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Pumarola_C-Flow_Conditional_Generative_Flow_Models_for_Images_and_3D_Point_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Hyperbolic Image Embeddings | Valentin Khrulkov, Leyla Mirvakhabova, Evgeniya Ustinova, Ivan Oseledets, Victor Lempitsky | Computer vision tasks such as image classification, image retrieval, and few-shot learning are currently dominated by Euclidean and spherical embeddings so that the final decisions about class belongings or the degree of similarity are made using linear hyperplanes, Euclidean distances, or spherical geodesic distances (cosine similarity). In this work, we demonstrate that in many practical scenarios, hyperbolic embeddings provide a better alternative. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Khrulkov_Hyperbolic_Image_Embeddings_CVPR_2020_paper.pdf | http://arxiv.org/abs/1904.02239 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Khrulkov_Hyperbolic_Image_Embeddings_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Khrulkov_Hyperbolic_Image_Embeddings_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Khrulkov_Hyperbolic_Image_Embeddings_CVPR_2020_supplemental.pdf | null | null |
Nested Scale-Editing for Conditional Image Synthesis | Lingzhi Zhang, Jiancong Wang, Yinshuang Xu, Jie Min, Tarmily Wen, James C. Gee, Jianbo Shi | We propose an image synthesis approach that provides stratified navigation in the latent code space. With a tiny amount of partial or very low-resolution image, our approach can consistently out-perform state-of-the-art counterparts in terms of generating the closest sampled image to the ground truth. We achieve this through scale-independent editing while expanding scale-specific diversity. Scale-independence is achieved with a nested scale disentanglement loss. Scale-specific diversity is created by incorporating a progressive diversification constraint. We introduce semantic persistency across the scales by sharing common latent codes. Together they provide better control of the image synthesis process. We evaluate the effectiveness of our proposed approach through various tasks, including image outpainting, image superresolution, and cross-domain image translation. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Nested_Scale-Editing_for_Conditional_Image_Synthesis_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.02038 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Nested_Scale-Editing_for_Conditional_Image_Synthesis_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Nested_Scale-Editing_for_Conditional_Image_Synthesis_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhang_Nested_Scale-Editing_for_CVPR_2020_supplemental.pdf | null | null |
Joint Spatial-Temporal Optimization for Stereo 3D Object Tracking | Peiliang Li, Jieqi Shi, Shaojie Shen | Directly learning multiple 3D objects motion from sequential images is difficult, while the geometric bundle adjustment lacks the ability to localize the invisible object centroid. To benefit from both the powerful object understanding skill from deep neural network meanwhile tackle precise geometry modeling for consistent trajectory estimation, we propose a joint spatial-temporal optimization-based stereo 3D object tracking method. From the network, we detect corresponding 2D bounding boxes on adjacent images and regress an initial 3D bounding box. Dense object cues (local depth and local coordinates) that associating to the object centroid are then predicted using a region-based network. Considering both the instant localization accuracy and motion consistency, our optimization models the relations between the object centroid and observed cues into a joint spatial-temporal error function. All historic cues will be summarized to contribute to the current estimation by a per-frame marginalization strategy without repeated computation. Quantitative evaluation on the KITTI tracking dataset shows our approach outperforms previous image-based 3D tracking methods by significant margins. We also report extensive results on multiple categories and larger datasets (KITTI raw and Argoverse Tracking) for future benchmarking. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Joint_Spatial-Temporal_Optimization_for_Stereo_3D_Object_Tracking_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.09305 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Joint_Spatial-Temporal_Optimization_for_Stereo_3D_Object_Tracking_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Joint_Spatial-Temporal_Optimization_for_Stereo_3D_Object_Tracking_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Joint_Spatial-Temporal_Optimization_CVPR_2020_supplemental.pdf | null | null |
Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation | Runfa Chen, Wenbing Huang, Binghui Huang, Fuchun Sun, Bin Fang | Unsupervised image-to-image translation is a central task in computer vision. Current translation frameworks will abandon the discriminator once the training process is completed. This paper contends a novel role of the discriminator by reusing it for encoding the images of the target domain. The proposed architecture, termed as NICE-GAN, exhibits two advantageous patterns over previous approaches: First, it is more compact since no independent encoding component is required; Second, this plug-in encoder is directly trained by the adversary loss, making it more informative and trained more effectively if a multi-scale discriminator is applied. The main issue in NICE-GAN is the coupling of translation with discrimination along the encoder, which could incur training inconsistency when we play the min-max game via GAN. To tackle this issue, we develop a decoupled training strategy by which the encoder is only trained when maximizing the adversary loss while keeping frozen otherwise. Extensive experiments on four popular benchmarks demonstrate the superior performance of NICE-GAN over state-of-the-art methods in terms of FID, KID, and also human preference. Comprehensive ablation studies are also carried out to isolate the validity of each proposed component. Our codes are available at https://github.com/alpc91/NICE-GAN-pytorch. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Reusing_Discriminators_for_Encoding_Towards_Unsupervised_Image-to-Image_Translation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.00273 | https://www.youtube.com/watch?v=NcFrBJTgW-c | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Reusing_Discriminators_for_Encoding_Towards_Unsupervised_Image-to-Image_Translation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Reusing_Discriminators_for_Encoding_Towards_Unsupervised_Image-to-Image_Translation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_Reusing_Discriminators_for_CVPR_2020_supplemental.pdf | null | null |
Learning Representations by Predicting Bags of Visual Words | Spyros Gidaris, Andrei Bursuc, Nikos Komodakis, Patrick Perez, Matthieu Cord | Self-supervised representation learning targets to learn convnet-based image representations from unlabeled data. Inspired by the success of NLP methods in this area, in this work we propose a self-supervised approach based on spatially dense image descriptions that encode discrete visual concepts, here called visual words. To build such discrete representations, we quantize the feature maps of a first pre-trained self-supervised convnet, over a k-means based vocabulary. Then, as a self-supervised task, we train another convnet to predict the histogram of visual words of an image (i.e., its Bag-of-Words representation) given as input a perturbed version of that image. The proposed task forces the convnet to learn perturbation-invariant and context-aware image features, useful for downstream image understanding tasks. We extensively evaluate our method and demonstrate very strong empirical results, e.g., our pre-trained self-supervised representations transfer better on detection task and similarly on classification over classes "unseen" during pre-training, when compared to the supervised case. This also shows that the process of image discretization into visual words can provide the basis for very powerful self-supervised approaches in the image domain, thus allowing further connections to be made to related methods from the NLP domain that have been extremely successful so far. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gidaris_Learning_Representations_by_Predicting_Bags_of_Visual_Words_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.12247 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gidaris_Learning_Representations_by_Predicting_Bags_of_Visual_Words_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gidaris_Learning_Representations_by_Predicting_Bags_of_Visual_Words_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Gidaris_Learning_Representations_by_CVPR_2020_supplemental.pdf | null | null |
Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds | Yongming Rao, Jiwen Lu, Jie Zhou | Local and global patterns of an object are closely related. Although each part of an object is incomplete, the underlying attributes about the object are shared among all parts, which makes reasoning the whole object from a single part possible. We hypothesize that a powerful representation of a 3D object should model the attributes that are shared between parts and the whole object, and distinguishable from other objects. Based on this hypothesis, we propose to learn point cloud representation by bidirectional reasoning between the local structures at different abstraction hierarchies and the global shape without human supervision. Experimental results on various benchmark datasets demonstrate the unsupervisedly learned representation is even better than supervised representation in discriminative power, generalization ability, and robustness. We show that unsupervisedly trained point cloud models can outperform their supervised counterparts on downstream classification tasks. Most notably, by simply increasing the channel width of an SSG PointNet++, our unsupervised model surpasses the state-of-the-art supervised methods on both synthetic and real-world 3D object classification datasets. We expect our observations to offer a new perspective on learning better representation from data structures instead of human annotations for point cloud understanding. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Rao_Global-Local_Bidirectional_Reasoning_for_Unsupervised_Representation_Learning_of_3D_Point_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12971 | https://www.youtube.com/watch?v=GYOD-Qnjdl8 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Rao_Global-Local_Bidirectional_Reasoning_for_Unsupervised_Representation_Learning_of_3D_Point_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Rao_Global-Local_Bidirectional_Reasoning_for_Unsupervised_Representation_Learning_of_3D_Point_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Rao_Global-Local_Bidirectional_Reasoning_CVPR_2020_supplemental.pdf | null | null |
Knowledge As Priors: Cross-Modal Knowledge Generalization for Datasets Without Superior Knowledge | Long Zhao, Xi Peng, Yuxiao Chen, Mubbasir Kapadia, Dimitris N. Metaxas | Cross-modal knowledge distillation deals with transferring knowledge from a model trained with superior modalities (Teacher) to another model trained with weak modalities (Student). Existing approaches require paired training examples exist in both modalities. However, accessing the data from superior modalities may not always be feasible. For example, in the case of 3D hand pose estimation, depth maps, point clouds, or stereo images usually capture better hand structures than RGB images, but most of them are expensive to be collected. In this paper, we propose a novel scheme to train the Student in a Target dataset where the Teacher is unavailable. Our key idea is to generalize the distilled cross-modal knowledge learned from a Source dataset, which contains paired examples from both modalities, to the Target dataset by modeling knowledge as priors on parameters of the Student. We name our method "Cross-Modal Knowledge Generalization" and demonstrate that our scheme results in competitive performance for 3D hand pose estimation on standard benchmark datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhao_Knowledge_As_Priors_Cross-Modal_Knowledge_Generalization_for_Datasets_Without_Superior_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00176 | https://www.youtube.com/watch?v=L7YB86Pcd6c | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Knowledge_As_Priors_Cross-Modal_Knowledge_Generalization_for_Datasets_Without_Superior_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Knowledge_As_Priors_Cross-Modal_Knowledge_Generalization_for_Datasets_Without_Superior_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhao_Knowledge_As_Priors_CVPR_2020_supplemental.pdf | null | null |
Large Scale Video Representation Learning via Relational Graph Clustering | Hyodong Lee, Joonseok Lee, Joe Yue-Hei Ng, Paul Natsev | Representation learning is widely applied for various tasks on multimedia data, e.g., retrieval and search. One approach for learning useful representation is by utilizing the relationships or similarities between examples. In this work, we explore two promising scalable representation learning approaches on video domain. With hierarchical graph clusters built upon video-to-video similarities, we propose: 1) smart negative sampling strategy that significantly boosts training efficiency with triplet loss, and 2) a pseudo-classification approach using the clusters as pseudo-labels. The embeddings trained with the proposed methods are competitive on multiple video understanding tasks, including related video retrieval and video annotation. Both of these proposed methods are highly scalable, as verified by experiments on large-scale datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lee_Large_Scale_Video_Representation_Learning_via_Relational_Graph_Clustering_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_Large_Scale_Video_Representation_Learning_via_Relational_Graph_Clustering_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_Large_Scale_Video_Representation_Learning_via_Relational_Graph_Clustering_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
ASLFeat: Learning Local Features of Accurate Shape and Localization | Zixin Luo, Lei Zhou, Xuyang Bai, Hongkai Chen, Jiahui Zhang, Yao Yao, Shiwei Li, Tian Fang, Long Quan | This work focuses on mitigating two limitations in the joint learning of local feature detectors and descriptors. First, the ability to estimate the local shape (scale, orientation, etc.) of feature points is often neglected during dense feature extraction, while the shape-awareness is crucial to acquire stronger geometric invariance. Second, the localization accuracy of detected keypoints is not sufficient to reliably recover camera geometry, which has become the bottleneck in tasks such as 3D reconstruction. In this paper, we present ASLFeat, with three light-weight yet effective modifications to mitigate above issues. First, we resort to deformable convolutional networks to densely estimate and apply local transformation. Second, we take advantage of the inherent feature hierarchy to restore spatial resolution and low-level details for accurate keypoint localization. Finally, we use a peakiness measurement to relate feature responses and derive more indicative detection scores. The effect of each modification is thoroughly studied, and the evaluation is extensively conducted across a variety of practical scenarios. State-of-the-art results are reported that demonstrate the superiority of our methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Luo_ASLFeat_Learning_Local_Features_of_Accurate_Shape_and_Localization_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.10071 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Luo_ASLFeat_Learning_Local_Features_of_Accurate_Shape_and_Localization_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Luo_ASLFeat_Learning_Local_Features_of_Accurate_Shape_and_Localization_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Luo_ASLFeat_Learning_Local_CVPR_2020_supplemental.pdf | null | null |
Video Super-Resolution With Temporal Group Attention | Takashi Isobe, Songjiang Li, Xu Jia, Shanxin Yuan, Gregory Slabaugh, Chunjing Xu, Ya-Li Li, Shengjin Wang, Qi Tian | Video super-resolution, which aims at producing a high-resolution video from its corresponding low-resolution version, has recently drawn increasing attention. In this work, we propose a novel method that can effectively incorporate temporal information in a hierarchical way. The input sequence is divided into several groups, with each one corresponding to a kind of frame rate. These groups provide complementary information to recover missing details in the reference frame, which is further integrated with an attention module and a deep intra-group fusion module. In addition, a fast spatial alignment is proposed to handle videos with large motion. Extensive results demonstrate the capability of the proposed model in handling videos with various motion. It achieves favorable performance against state-of-the-art methods on several benchmark datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Isobe_Video_Super-Resolution_With_Temporal_Group_Attention_CVPR_2020_paper.pdf | http://arxiv.org/abs/2007.10595 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Isobe_Video_Super-Resolution_With_Temporal_Group_Attention_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Isobe_Video_Super-Resolution_With_Temporal_Group_Attention_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style | Chaitanya Patel, Zhouyingcheng Liao, Gerard Pons-Moll | In this paper, we present TailorNet, a neural model which predicts clothing deformation in 3D as a function of three factors: pose, shape and style (garment geometry), while retaining wrinkle detail. This goes beyond prior models, which are either specific to one style and shape, or generalize to different shapes producing smooth results, despite being style specific. Our hypothesis is that (even non-linear) combinations of examples smoothes out high frequency components such as fine-wrinkles, which makes learning the three factors jointly hard. At the heart of our technique is a decomposition of deformation into a high frequency and a low frequency component. While the low-frequency component is predicted from pose, shape and style parameters with an MLP, the high-frequency component is predicted with a mixture of shape-style specific pose models. The weights of the mixture are computed with a narrow bandwidth kernel to guarantee that only predictions with similar high-frequency patterns are combined. The style variation is obtained by computing, in a canonical pose, a subspace of deformation, which satisfies physical constraints such as inter-penetration, and draping on the body. TailorNet delivers 3D garments which retain the wrinkles from the physics based simulations (PBS) it is learned from, while running more than 1000 times faster. In contrast to classical PBS, TailorNet is easy to use and fully differentiable, which is crucial for computer vision and learning algorithms. Several experiments demonstrate TailorNet produces more realistic results than prior work, and even generates temporally coherent deformations on sequences of the AMASS dataset, despite being trained on static poses from a different dataset. To stimulate further research in this direction, we will make a dataset consisting of 55800 frames, as well as our model publicly available at https://virtualhumans.mpi-inf.mpg.de/tailornet/. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Patel_TailorNet_Predicting_Clothing_in_3D_as_a_Function_of_Human_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=vg7a52zObjs | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Patel_TailorNet_Predicting_Clothing_in_3D_as_a_Function_of_Human_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Patel_TailorNet_Predicting_Clothing_in_3D_as_a_Function_of_Human_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Patel_TailorNet_Predicting_Clothing_CVPR_2020_supplemental.pdf | null | null |
CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition | Yuge Huang, Yuhan Wang, Ying Tai, Xiaoming Liu, Pengcheng Shen, Shaoxin Li, Jilin Li, Feiyue Huang | As an emerging topic in face recognition, designing margin-based loss functions can increase the feature margin between different classes for enhanced discriminability. More recently, the idea of mining-based strategies is adopted to emphasize the misclassified samples, achieving promising results. However, during the entire training process, the prior methods either do not explicitly emphasize the sample based on its importance that renders the hard samples not fully exploited; or explicitly emphasize the effects of semi-hard/hard samples even at the early training stage that may lead to convergence issue. In this work, we propose a novel Adaptive Curriculum Learning loss (CurricularFace) that embeds the idea of curriculum learning into the loss function to achieve a novel training strategy for deep face recognition, which mainly addresses easy samples in the early training stage and hard ones in the later stage. Specifically, our CurricularFace adaptively adjusts the relative importance of easy and hard samples during different training stages. In each stage, different samples are assigned with different importance according to their corresponding difficultness. Extensive experimental results on popular benchmarks demonstrate the superiority of our CurricularFace over the state-of-the-art competitors. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Huang_CurricularFace_Adaptive_Curriculum_Learning_Loss_for_Deep_Face_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00288 | https://www.youtube.com/watch?v=87G6umX8HO4 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_CurricularFace_Adaptive_Curriculum_Learning_Loss_for_Deep_Face_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_CurricularFace_Adaptive_Curriculum_Learning_Loss_for_Deep_Face_Recognition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
On the Detection of Digital Face Manipulation | Hao Dang, Feng Liu, Joel Stehouwer, Xiaoming Liu, Anil K. Jain | Detecting manipulated facial images and videos is an increasingly important topic in digital media forensics. As advanced face synthesis and manipulation methods are made available, new types of fake face representations are being created which have raised significant concerns for their use in social media. Hence, it is crucial to detect manipulated face images and localize manipulated regions. Instead of simply using multi-task learning to simultaneously detect manipulated images and predict the manipulated mask (regions), we propose to utilize an attention mechanism to process and improve the feature maps for the classification task. The learned attention maps highlight the informative regions to further improve the binary classification (genuine face v. fake face), and also visualize the manipulated regions. To enable our study of manipulated face detection and localization, we collect a large-scale database that contains numerous types of facial forgeries. With this dataset, we perform a thorough analysis of data-driven fake face detection. We show that the use of an attention mechanism improves facial forgery detection and manipulated region localization. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Dang_On_the_Detection_of_Digital_Face_Manipulation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1910.01717 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Dang_On_the_Detection_of_Digital_Face_Manipulation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Dang_On_the_Detection_of_Digital_Face_Manipulation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Sketch-BERT: Learning Sketch Bidirectional Encoder Representation From Transformers by Self-Supervised Learning of Sketch Gestalt | Hangyu Lin, Yanwei Fu, Xiangyang Xue, Yu-Gang Jiang | Previous researches of sketches often considered sketches in pixel format and leveraged CNN based models in the sketch understanding. Fundamentally, a sketch is stored as a sequence of data points, a vector format representation, rather than the photo-realistic image of pixels. SketchRNN studied a generative neural representation for sketches of vector format by Long Short Term Memory networks (LSTM). Unfortunately, the representation learned by SketchRNN is primarily for the generation tasks, rather than the other tasks of recognition and retrieval of sketches. To this end and inspired by the recent BERT model, we present a model of learning Sketch Bidirectional Encoder Representation from Transformer (Sketch-BERT). We generalize BERT to sketch domain, with the novel proposed components and pre-training algorithms, including the newly designed sketch embedding networks, and the self-supervised learning of sketch gestalt. Particularly, towards the pre-training task, we present a novel Sketch Gestalt Model (SGM) to help train the Sketch-BERT. Experimentally, we show that the learned representation of Sketch-BERT can help and improve the performance of the downstream tasks of sketch recognition, sketch retrieval, and sketch gestalt. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lin_Sketch-BERT_Learning_Sketch_Bidirectional_Encoder_Representation_From_Transformers_by_Self-Supervised_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=-Rrx761syWo | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Sketch-BERT_Learning_Sketch_Bidirectional_Encoder_Representation_From_Transformers_by_Self-Supervised_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Sketch-BERT_Learning_Sketch_Bidirectional_Encoder_Representation_From_Transformers_by_Self-Supervised_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lin_Sketch-BERT_Learning_Sketch_CVPR_2020_supplemental.pdf | null | null |
Decoupled Representation Learning for Skeleton-Based Gesture Recognition | Jianbo Liu, Yongcheng Liu, Ying Wang, Veronique Prinet, Shiming Xiang, Chunhong Pan | Skeleton-based gesture recognition is very challenging, as the high-level information in gesture is expressed by a sequence of complexly composite motions. Previous works often learn all the motions with a single model. In this paper, we propose to decouple the gesture into hand posture variations and hand movements, which are then modeled separately. For the former, the skeleton sequence is embedded into a 3D hand posture evolution volume (HPEV) to represent fine-grained posture variations. For the latter, the shifts of hand center and fingertips are arranged as a 2D hand movement map (HMM) to capture holistic movements. To learn from the two inhomogeneous representations for gesture recognition, we propose an end-to-end two-stream network. The HPEV stream integrates both spatial layout and temporal evolution information of hand postures by a dedicated 3D CNN, while the HMM stream develops an efficient 2D CNN to extract hand movement features. Eventually, the predictions of the two streams are aggregated with high efficiency. Extensive experiments on SHREC'17 Track, DHG-14/28 and FPHA datasets demonstrate that our method is competitive with the state-of-the-art. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Decoupled_Representation_Learning_for_Skeleton-Based_Gesture_Recognition_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Decoupled_Representation_Learning_for_Skeleton-Based_Gesture_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Decoupled_Representation_Learning_for_Skeleton-Based_Gesture_Recognition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Analyzing and Improving the Image Quality of StyleGAN | Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, Timo Aila | The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably attribute a generated image to a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Karras_Analyzing_and_Improving_the_Image_Quality_of_StyleGAN_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.04958 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Karras_Analyzing_and_Improving_the_Image_Quality_of_StyleGAN_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Karras_Analyzing_and_Improving_the_Image_Quality_of_StyleGAN_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Karras_Analyzing_and_Improving_CVPR_2020_supplemental.pdf | null | null |
Learning to Dress 3D People in Generative Clothing | Qianli Ma, Jinlong Yang, Anurag Ranjan, Sergi Pujades, Gerard Pons-Moll, Siyu Tang, Michael J. Black | Three-dimensional human body models are widely used in the analysis of human pose and motion. Existing models, however, are learned from minimally-clothed 3D scans and thus do not generalize to the complexity of dressed people in common images and videos. Additionally, current models lack the expressive power needed to represent the complex non-linear geometry of pose-dependent clothing shapes. To address this, we learn a generative 3D mesh model of clothed people from 3D scans with varying pose and clothing. Specifically, we train a conditional Mesh-VAE-GAN to learn the clothing deformation from the SMPL body model, making clothing an additional term in SMPL. Our model is conditioned on both pose and clothing type, giving the ability to draw samples of clothing to dress different body shapes in a variety of styles and poses. To preserve wrinkle detail, our Mesh-VAE-GAN extends patchwise discriminators to 3D meshes. Our model, named CAPE, represents global shape and fine local structure, effectively extending the SMPL body model to clothing. To our knowledge, this is the first generative model that directly dresses 3D human body meshes and generalizes to different poses. The model, code and data are available for research purposes at https://cape.is.tue.mpg.de. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ma_Learning_to_Dress_3D_People_in_Generative_Clothing_CVPR_2020_paper.pdf | http://arxiv.org/abs/1907.13615 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ma_Learning_to_Dress_3D_People_in_Generative_Clothing_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ma_Learning_to_Dress_3D_People_in_Generative_Clothing_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Ma_Learning_to_Dress_CVPR_2020_supplemental.pdf | https://cove.thecvf.com/datasets/361 | null |
Cross-Modal Pattern-Propagation for RGB-T Tracking | Chaoqun Wang, Chunyan Xu, Zhen Cui, Ling Zhou, Tong Zhang, Xiaoya Zhang, Jian Yang | Motivated by our observations on RGB-T data that pattern correlations are high-frequently recurred across modalities also along sequence frames, in this paper, we propose a cross-modal pattern-propagation (CMPP) tracking framework to diffuse instance patterns across RGB-T data on spatial domain as well as temporal domain. To bridge RGB-T modalities, the cross-modal correlations on intra-modal paired pattern-affinities are derived to reveal those latent cues between heterogenous modalities. Through the correlations, the useful patterns may be mutually propagated between RGB-T modalities so as to fulfill inter-modal pattern-propagation. Further, considering the temporal continuity of sequence frames, we adopt the spirit of pattern propagation to dynamic temporal domain, in which long-term historical contexts are adaptively correlated and propagated into the current frame for more effective information inheritance. Extensive experiments demonstrate that the effectiveness of our proposed CMPP, and the new state-of-the-art results are achieved with the significant improvements on two RGB-T object tracking benchmarks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Cross-Modal_Pattern-Propagation_for_RGB-T_Tracking_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Cross-Modal_Pattern-Propagation_for_RGB-T_Tracking_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Cross-Modal_Pattern-Propagation_for_RGB-T_Tracking_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Channel Attention Based Iterative Residual Learning for Depth Map Super-Resolution | Xibin Song, Yuchao Dai, Dingfu Zhou, Liu Liu, Wei Li, Hongdong Li, Ruigang Yang | Despite the remarkable progresses made in deep learning based depth map super-resolution (DSR), how to tackle real-world degradation in low-resolution (LR) depth maps remains a major challenge. Existing DSR model is generally trained and tested on synthetic dataset, which is very different from what would get from a real depth sensor. In this paper, we argue that DSR models trained under this setting are restrictive and not effective in dealing with realworld DSR tasks. We make two contributions in tackling real-world degradation of different depth sensors. First, we propose to classify the generation of LR depth maps into two types: non-linear downsampling with noise and interval downsampling, for which DSR models are learned correspondingly. Second, we propose a new framework for real-world DSR, which consists of four modules : 1) An iterative residual learning module with deep supervision to learn effective high-frequency components of depth maps in a coarse-to-fine manner; 2) A channel attention strategy to enhance channels with abundant high-frequency components; 3) A multi-stage fusion module to effectively reexploit the results in the coarse-to-fine process; and 4) A depth refinement module to improve the depth map by TGV regularization and input loss. Extensive experiments on benchmarking datasets demonstrate the superiority of our method over current state-of-the-art DSR methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Song_Channel_Attention_Based_Iterative_Residual_Learning_for_Depth_Map_Super-Resolution_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.01469 | https://www.youtube.com/watch?v=BHOTAFiw0tM | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Song_Channel_Attention_Based_Iterative_Residual_Learning_for_Depth_Map_Super-Resolution_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Song_Channel_Attention_Based_Iterative_Residual_Learning_for_Depth_Map_Super-Resolution_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Averaging Essential and Fundamental Matrices in Collinear Camera Settings | Amnon Geifman, Yoni Kasten, Meirav Galun, Ronen Basri | Global methods to Structure from Motion have gained popularity in recent years. A significant drawback of global methods is their sensitivity to collinear camera settings. In this paper, we introduce an analysis and algorithms for averaging bifocal tensors (essential or fundamental matrices) when either subsets or all of the camera centers are collinear. We provide a complete spectral characterization of bifocal tensors in collinear scenarios and further propose two averaging algorithms. The first algorithm uses rank constrained minimization to recover camera matrices in fully collinear settings. The second algorithm enriches the set of possibly mixed collinear and non-collinear cameras with additional, "virtual cameras," which are placed in general position, enabling the application of existing averaging methods to the enriched set of bifocal tensors. Our algorithms are shown to achieve state of the art results on various benchmarks that include autonomous car datasets and unordered image collections in both calibrated and unclibrated settings. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Geifman_Averaging_Essential_and_Fundamental_Matrices_in_Collinear_Camera_Settings_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.00254 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Geifman_Averaging_Essential_and_Fundamental_Matrices_in_Collinear_Camera_Settings_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Geifman_Averaging_Essential_and_Fundamental_Matrices_in_Collinear_Camera_Settings_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Geifman_Averaging_Essential_and_CVPR_2020_supplemental.pdf | null | null |
Deep Spatial Gradient and Temporal Depth Learning for Face Anti-Spoofing | Zezheng Wang, Zitong Yu, Chenxu Zhao, Xiangyu Zhu, Yunxiao Qin, Qiusheng Zhou, Feng Zhou, Zhen Lei | Face anti-spoofing is critical to the security of face recognition systems. Depth supervised learning has been proven as one of the most effective methods for face anti-spoofing. Despite the great success, most previous works still formulate the problem as a single-frame multi-task one by simply augmenting the loss with depth, while neglecting the detailed fine-grained information and the interplay between facial depths and moving patterns. In contrast, we design a new approach to detect presentation attacks from multiple frames based on two insights: 1) detailed discriminative clues (e.g., spatial gradient magnitude) between living and spoofing face may be discarded through stacked vanilla convolutions, and 2) the dynamics of 3D moving faces provide important clues in detecting the spoofing faces. The proposed method is able to capture discriminative details via Residual Spatial Gradient Block (RSGB) and encode spatio-temporal information from Spatio-Temporal Propagation Module (STPM) efficiently. Moreover, a novel Contrastive Depth Loss is presented for more accurate depth supervision. To assess the efficacy of our method, we also collect a Double-modal Anti-spoofing Dataset (DMAD) which provides actual depth for each sample. The experiments demonstrate that the proposed approach achieves state-of-the-art results on five benchmark datasets including OULU-NPU, SiW, CASIA-MFSD, Replay-Attack, and the new DMAD. Codes will be available at https://github.com/clks-wzz/FAS-SGTD. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Deep_Spatial_Gradient_and_Temporal_Depth_Learning_for_Face_Anti-Spoofing_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.08061 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Deep_Spatial_Gradient_and_Temporal_Depth_Learning_for_Face_Anti-Spoofing_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Deep_Spatial_Gradient_and_Temporal_Depth_Learning_for_Face_Anti-Spoofing_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_Deep_Spatial_Gradient_CVPR_2020_supplemental.pdf | null | null |
Instance-Aware Image Colorization | Jheng-Wei Su, Hung-Kuo Chu, Jia-Bin Huang | Image colorization is inherently an ill-posed problem with multi-modal uncertainty. Previous methods leverage the deep neural network to map input grayscale images to plausible color outputs directly. Although these learning-based methods have shown impressive performance, they usually fail on the input images that contain multiple objects. The leading cause is that existing models perform learning and colorization on the entire image. In the absence of a clear figure-ground separation, these models cannot effectively locate and learn meaningful object-level semantics. In this paper, we propose a method for achieving instance-aware colorization. Our network architecture leverages an off-the-shelf object detector to obtain cropped object images and uses an instance colorization network to extract object-level features. We use a similar network to extract the full-image features and apply a fusion module to full object-level and image-level features to predict the final colors. Both colorization networks and fusion modules are learned from a large-scale dataset. Experimental results show that our work outperforms existing methods on different quality metrics and achieves state-of-the-art performance on image colorization. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Su_Instance-Aware_Image_Colorization_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.10825 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Su_Instance-Aware_Image_Colorization_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Su_Instance-Aware_Image_Colorization_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
ReDA:Reinforced Differentiable Attribute for 3D Face Reconstruction | Wenbin Zhu, HsiangTao Wu, Zeyu Chen, Noranart Vesdapunt, Baoyuan Wang | The key challenge for 3D face shape reconstruction is to build the correct dense face correspondence between the deformable mesh and the single input image. Given the ill-posed nature, previous works heavily rely on prior knowledge (such as 3DMM [2]) to reduce depth ambiguity. Although impressive result has been made recently [42, 14, 8], there is still a large room to improve the correspondence so that projected face shape better aligns with the silhouette of each face region (i.e, eye, mouth, nose, cheek, etc.) on the image. To further reduce the ambiguities, we present a novel framework called "Reinforced Differentiable Attributes" ("ReDA") which is more general and effective than previous Differentiable Rendering ("DR"). Specifically, we first extend from color to more broad attributes, including the depth and the face parsing mask. Secondly, unlike the previous Z-buffer rendering, we make the rendering to be more differentiable through a set of convolution operations with multi-scale kernel sizes. In the meanwhile, to make "ReDA" to be more successful for 3D face recon-struction, we further introduce a new free-form deformation layer that sits on top of 3DMM to enjoy both the prior knowledge and out-of-space modeling. Both techniques can be easily integrated into existing 3D face reconstruction pipeline. Extensive experiments on both RGB and RGB-D datasets show that our approach outperforms prior arts. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhu_ReDAReinforced_Differentiable_Attribute_for_3D_Face_Reconstruction_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_ReDAReinforced_Differentiable_Attribute_for_3D_Face_Reconstruction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_ReDAReinforced_Differentiable_Attribute_for_3D_Face_Reconstruction_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Towards Global Explanations of Convolutional Neural Networks With Concept Attribution | Weibin Wu, Yuxin Su, Xixian Chen, Shenglin Zhao, Irwin King, Michael R. Lyu, Yu-Wing Tai | With the growing prevalence of convolutional neural networks (CNNs), there is an urgent demand to explain their behaviors. Global explanations contribute to understanding model predictions on a whole category of samples, and thus have attracted increasing interest recently. However, existing methods overwhelmingly conduct separate input attribution or rely on local approximations of models, making them fail to offer faithful global explanations of CNNs. To overcome such drawbacks, we propose a novel two-stage framework, Attacking for Interpretability (AfI), which explains model decisions in terms of the importance of user-defined concepts. AfI first conducts a feature occlusion analysis, which resembles a process of attacking models to derive the category-wide importance of different features. We then map the feature importance to concept importance through ad-hoc semantic tasks. Experimental results confirm the effectiveness of AfI and its superiority in providing more accurate estimations of concept importance than existing proposals. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wu_Towards_Global_Explanations_of_Convolutional_Neural_Networks_With_Concept_Attribution_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=pJj98eWrpq8 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_Towards_Global_Explanations_of_Convolutional_Neural_Networks_With_Concept_Attribution_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_Towards_Global_Explanations_of_Convolutional_Neural_Networks_With_Concept_Attribution_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wu_Towards_Global_Explanations_CVPR_2020_supplemental.pdf | null | null |
Cross-Domain Face Presentation Attack Detection via Multi-Domain Disentangled Representation Learning | Guoqing Wang, Hu Han, Shiguang Shan, Xilin Chen | Face presentation attack detection (PAD) has been an urgent problem to be solved in the face recognition systems. Conventional approaches usually assume the testing and training are within the same domain; as a result, they may not generalize well into unseen scenarios because the representations learned for PAD may overfit to the subjects in the training set. In light of this, we propose an efficient disentangled representation learning for cross-domain face PAD. Our approach consists of disentangled representation learning (DR-Net) and multi-domain learning (MD-Net). DR-Net learns a pair of encoders via generative models that can disentangle PAD informative features from subject discriminative features. The disentangled features from different domains are fed to MD-Net which learns domain-independent features for the final cross-domain face PAD task. Extensive experiments on several public datasets validate the effectiveness of the proposed approach for cross-domain PAD. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Cross-Domain_Face_Presentation_Attack_Detection_via_Multi-Domain_Disentangled_Representation_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.01959 | https://www.youtube.com/watch?v=Yp8eyp8s4Ns | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Cross-Domain_Face_Presentation_Attack_Detection_via_Multi-Domain_Disentangled_Representation_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Cross-Domain_Face_Presentation_Attack_Detection_via_Multi-Domain_Disentangled_Representation_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Time Flies: Animating a Still Image With Time-Lapse Video As Reference | Chia-Chi Cheng, Hung-Yu Chen, Wei-Chen Chiu | Time-lapse videos usually perform eye-catching appearances but are often hard to create. In this paper, we propose a self-supervised end-to-end model to generate the time-lapse video from a single image and a reference video. Our key idea is to extract both the style and the features of temporal variation from the reference video, and transfer them onto the input image. To ensure both the temporal consistency and realness of our resultant videos, we introduce several novel designs in our architecture, including classwise NoiseAdaIN, flow loss, and the video discriminator. In comparison to the baselines of state-of-the-art style transfer approaches, our proposed method is not only efficient in computation but also able to create more realistic and temporally smooth time-lapse video of a still image, with its temporal variation consistent to the reference. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cheng_Time_Flies_Animating_a_Still_Image_With_Time-Lapse_Video_As_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cheng_Time_Flies_Animating_a_Still_Image_With_Time-Lapse_Video_As_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cheng_Time_Flies_Animating_a_Still_Image_With_Time-Lapse_Video_As_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
PandaNet: Anchor-Based Single-Shot Multi-Person 3D Pose Estimation | Abdallah Benzine, Florian Chabot, Bertrand Luvison, Quoc Cuong Pham, Catherine Achard | Recently, several deep learning models have been proposed for 3D human pose estimation. Nevertheless, most of these approaches only focus on the single-person case or estimate 3D pose of a few people at high resolution. Furthermore, many applications such as autonomous driving or crowd analysis require pose estimation of a large number of people possibly at low-resolution. In this work, we present PandaNet (Pose estimAtioN and Dectection Anchor-based Network), a new single-shot, anchor-based and multi-person 3D pose estimation approach. The proposed model performs bounding box detection and, for each detected person, 2D and 3D pose regression into a single forward pass. It does not need any post-processing to regroup joints since the network predicts a full 3D pose for each bounding box and allows the pose estimation of a possibly large number of people at low resolution. To manage people overlapping, we introduce a Pose-Aware Anchor Selection strategy. Moreover, as imbalance exists between different people sizes in the image, and joints coordinates have different uncertainties depending on these sizes, we propose a method to automatically optimize weights associated to different people scales and joints for efficient training. PandaNet surpasses previous single-shot methods on several challenging datasets: a multi-person urban virtual but very realistic dataset (JTA Dataset), and two real world 3D multi-person datasets (CMU Panoptic and MuPoTS-3D). | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Benzine_PandaNet_Anchor-Based_Single-Shot_Multi-Person_3D_Pose_Estimation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Benzine_PandaNet_Anchor-Based_Single-Shot_Multi-Person_3D_Pose_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Benzine_PandaNet_Anchor-Based_Single-Shot_Multi-Person_3D_Pose_Estimation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Benzine_PandaNet_Anchor-Based_Single-Shot_CVPR_2020_supplemental.zip | null | null |
Modeling the Background for Incremental Learning in Semantic Segmentation | Fabio Cermelli, Massimiliano Mancini, Samuel Rota Bulo, Elisa Ricci, Barbara Caputo | Despite their effectiveness in a wide range of tasks, deep architectures suffer from some important limitations. In particular, they are vulnerable to catastrophic forgetting, i.e. they perform poorly when they are required to update their model as new classes are available but the original training set is not retained. This paper addresses this problem in the context of semantic segmentation. Current strategies fail on this task because they do not consider a peculiar aspect of semantic segmentation: since each training step provides annotation only for a subset of all possible classes, pixels of the background class (i.e. pixels that do not belong to any other classes) exhibit a semantic distribution shift. In this work we revisit classical incremental learning methods, proposing a new distillation-based framework which explicitly accounts for this shift. Furthermore, we introduce a novel strategy to initialize classifier's parameters, thus preventing biased predictions toward the background class. We demonstrate the effectiveness of our approach with an extensive evaluation on the Pascal-VOC 2012 and ADE20K datasets, significantly outperforming state of the art incremental learning methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cermelli_Modeling_the_Background_for_Incremental_Learning_in_Semantic_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.00718 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cermelli_Modeling_the_Background_for_Incremental_Learning_in_Semantic_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cermelli_Modeling_the_Background_for_Incremental_Learning_in_Semantic_Segmentation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Cermelli_Modeling_the_Background_CVPR_2020_supplemental.zip | null | null |
F-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation | Konstantin Sofiiuk, Ilia Petrov, Olga Barinova, Anton Konushin | Deep neural networks have become a mainstream approach to interactive segmentation. As we show in our experiments, while for some images a trained network provides accurate segmentation result with just a few clicks, for some unknown objects it cannot achieve satisfactory result even with a large amount of user input. Recently proposed backpropagating refinement scheme (BRS) introduces an optimization problem for interactive segmentation that results in significantly better performance for the hard cases. At the same time, BRS requires running forward and backward pass through a deep network several times that leads to significantly increased computational budget per click compared to other methods. We propose f-BRS (feature backpropagating refinement scheme) that solves an optimization problem with respect to auxiliary variables instead of the network inputs, and requires running forward and backward passes just for a small part of a network. Experiments on GrabCut, Berkeley, DAVIS and SBD datasets set new state-of-the-art at an order of magnitude lower time per click compared to original BRS. The code and trained models are available at https://github.com/saic-vul/fbrs_interactive_segmentation. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Sofiiuk_F-BRS_Rethinking_Backpropagating_Refinement_for_Interactive_Segmentation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Sofiiuk_F-BRS_Rethinking_Backpropagating_Refinement_for_Interactive_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Sofiiuk_F-BRS_Rethinking_Backpropagating_Refinement_for_Interactive_Segmentation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Sofiiuk_F-BRS_Rethinking_Backpropagating_CVPR_2020_supplemental.pdf | null | null |
Steering Self-Supervised Feature Learning Beyond Local Pixel Statistics | Simon Jenni, Hailin Jin, Paolo Favaro | We introduce a novel principle for self-supervised feature learning based on the discrimination of specific transformations of an image. We argue that the generalization capability of learned features depends on what image neighborhood size is sufficient to discriminate different image transformations: The larger the required neighborhood size and the more global the image statistics that the feature can describe. An accurate description of global image statistics allows to better represent the shape and configuration of objects and their context, which ultimately generalizes better to new tasks such as object classification and detection. This suggests a criterion to choose and design image transformations. Based on this criterion, we introduce a novel image transformation that we call limited context inpainting (LCI). This transformation inpaints an image patch conditioned only on a small rectangular pixel boundary (the limited context). Because of the limited boundary information, the inpainter can learn to match local pixel statistics, but is unlikely to match the global statistics of the image. We claim that the same principle can be used to justify the performance of transformations such as image rotations and warping. Indeed, we demonstrate experimentally that learning to discriminate transformations such as LCI, image warping and rotations, yields features with state of the art generalization capabilities on several datasets such as Pascal VOC, STL-10, CelebA, and ImageNet. Remarkably, our trained features achieve a performance on Places on par with features trained through supervised learning with ImageNet labels. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jenni_Steering_Self-Supervised_Feature_Learning_Beyond_Local_Pixel_Statistics_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.02331 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jenni_Steering_Self-Supervised_Feature_Learning_Beyond_Local_Pixel_Statistics_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jenni_Steering_Self-Supervised_Feature_Learning_Beyond_Local_Pixel_Statistics_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Jenni_Steering_Self-Supervised_Feature_CVPR_2020_supplemental.pdf | null | null |
Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild | Dominik Kulon, Riza Alp Guler, Iasonas Kokkinos, Michael M. Bronstein, Stefanos Zafeiriou | We introduce a simple and effective network architecture for monocular 3D hand pose estimation consisting of an image encoder followed by a mesh convolutional decoder that is trained through a direct 3D hand mesh reconstruction loss. We train our network by gathering a large-scale dataset of hand action in YouTube videos and use it as a source of weak supervision. Our weakly-supervised mesh convolutions-based system largely outperforms state-of-the-art methods, even halving the errors on the in the wild benchmark. The dataset and additional resources are available at https://arielai.com/mesh_hands. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kulon_Weakly-Supervised_Mesh-Convolutional_Hand_Reconstruction_in_the_Wild_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.01946 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kulon_Weakly-Supervised_Mesh-Convolutional_Hand_Reconstruction_in_the_Wild_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kulon_Weakly-Supervised_Mesh-Convolutional_Hand_Reconstruction_in_the_Wild_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task | Aritra Bhowmik, Stefan Gumhold, Carsten Rother, Eric Brachmann | We address a core problem of computer vision: Detection and description of 2D feature points for image matching. For a long time, hand-crafted designs, like the seminal SIFT algorithm, were unsurpassed in accuracy and efficiency. Recently, learned feature detectors emerged that implement detection and description using neural networks. Training these networks usually resorts to optimizing low-level matching scores, often pre-defining sets of image patches which should or should not match, or which should or should not contain key points. Unfortunately, increased accuracy for these low-level matching scores does not necessarily translate to better performance in high-level vision tasks. We propose a new training methodology which embeds the feature detector in a complete vision pipeline, and where the learnable parameters are trained in an end-to-end fashion. We overcome the discrete nature of key point selection and descriptor matching using principles from reinforcement learning. As an example, we address the task of relative pose estimation between a pair of images. We demonstrate that the accuracy of a state-of-the-art learning-based feature detector can be increased when trained for the task it is supposed to solve at test time. Our training methodology poses little restrictions on the task to learn, and works for any architecture which predicts key point heat maps, and descriptors for key point locations. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Bhowmik_Reinforced_Feature_Points_Optimizing_Feature_Detection_and_Description_for_a_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.00623 | https://www.youtube.com/watch?v=_Zbbvr5jGhQ | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Bhowmik_Reinforced_Feature_Points_Optimizing_Feature_Detection_and_Description_for_a_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Bhowmik_Reinforced_Feature_Points_Optimizing_Feature_Detection_and_Description_for_a_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
ProAlignNet: Unsupervised Learning for Progressively Aligning Noisy Contours | VSR Veeravasarapu, Abhishek Goel, Deepak Mittal, Maneesh Singh | Contour shape alignment is a fundamental but challenging problem in computer vision, especially when the observations are partial, noisy, and largely misaligned. Recent ConvNet-based architectures that were proposed to align image structures tend to fail with contour representation of shapes, mostly due to the use of proximity-insensitive pixel-wise similarity measures as loss functions in their training processes. This work presents a novel ConvNet, "ProAlignNet," that accounts for large scale misalignments and complex transformations between the contour shapes. It infers the warp parameters in a multi-scale fashion with progressively increasing complex transformations over increasing scales. It learns --without supervision-- to align contours, agnostic to noise and missing parts, by training with a novel loss function which is derived an upperbound of a proximity-sensitive and local shape-dependent similarity metric that uses classical Morphological Chamfer Distance Transform. We evaluate the reliability of these proposals on a simulated MNIST noisy contours dataset via some basic sanity check experiments. Next, we demonstrate the effectiveness of the proposed models in two real-world applications of (i) aligning geo-parcel data to aerial image maps and (ii) refining coarsely annotated segmentation labels. In both applications, the proposed models consistently perform superior to state-of-the-art methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Veeravasarapu_ProAlignNet_Unsupervised_Learning_for_Progressively_Aligning_Noisy_Contours_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.11546 | https://www.youtube.com/watch?v=0L7CUIDZ46c | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Veeravasarapu_ProAlignNet_Unsupervised_Learning_for_Progressively_Aligning_Noisy_Contours_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Veeravasarapu_ProAlignNet_Unsupervised_Learning_for_Progressively_Aligning_Noisy_Contours_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Veeravasarapu_ProAlignNet_Unsupervised_Learning_CVPR_2020_supplemental.pdf | null | null |
Attentive Normalization for Conditional Image Generation | Yi Wang, Ying-Cong Chen, Xiangyu Zhang, Jian Sun, Jiaya Jia | Traditional convolution-based generative adversarial networks synthesize images based on hierarchical local operations, where long-range dependency relation is implicitly modeled with a Markov chain. It is still not sufficient for categories with complicated structures. In this paper, we characterize long-range dependence with attentive normalization (AN), which is an extension to traditional instance normalization. Specifically, the input feature map is softly divided into several regions based on its internal semantic similarity, which are respectively normalized. It enhances consistency between distant regions with semantic correspondence. Compared with self-attention GAN, our attentive normalization does not need to measure the correlation of all locations, and thus can be directly applied to large-size feature maps without much computational burden. Extensive experiments on class-conditional image generation and semantic inpainting verify the efficacy of our proposed module. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Attentive_Normalization_for_Conditional_Image_Generation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.03828 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Attentive_Normalization_for_Conditional_Image_Generation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Attentive_Normalization_for_Conditional_Image_Generation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_Attentive_Normalization_for_CVPR_2020_supplemental.pdf | null | null |
Learning by Analogy: Reliable Supervision From Transformations for Unsupervised Optical Flow Estimation | Liang Liu, Jiangning Zhang, Ruifei He, Yong Liu, Yabiao Wang, Ying Tai, Donghao Luo, Chengjie Wang, Jilin Li, Feiyue Huang | Unsupervised learning of optical flow, which leverages the supervision from view synthesis, has emerged as a promising alternative to supervised methods. However, the objective of unsupervised learning is likely to be unreliable in challenging scenes. In this work, we present a framework to use more reliable supervision from transformations. It simply twists the general unsupervised learning pipeline by running another forward pass with transformed data from augmentation, along with using transformed predictions of original data as the self-supervision signal. Besides, we further introduce a lightweight network with multiple frames by a highly-shared flow decoder. Our method consistently gets a leap of performance on several benchmarks with the best accuracy among deep unsupervised methods. Also, our method achieves competitive results to recent fully supervised methods while with much fewer parameters. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Learning_by_Analogy_Reliable_Supervision_From_Transformations_for_Unsupervised_Optical_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13045 | https://www.youtube.com/watch?v=iwHxCd8LESo | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Learning_by_Analogy_Reliable_Supervision_From_Transformations_for_Unsupervised_Optical_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Learning_by_Analogy_Reliable_Supervision_From_Transformations_for_Unsupervised_Optical_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Towards Better Generalization: Joint Depth-Pose Learning Without PoseNet | Wang Zhao, Shaohui Liu, Yezhi Shu, Yong-Jin Liu | In this work, we tackle the essential problem of scale inconsistency for self supervised joint depth-pose learning. Most existing methods assume that a consistent scale of depth and pose can be learned across all input samples, which makes the learning problem harder, resulting in degraded performance and limited generalization in indoor environments and long-sequence visual odometry application. To address this issue, we propose a novel system that explicitly disentangles scale from the network estimation. Instead of relying on PoseNet architecture, our method recovers relative pose by directly solving fundamental matrix from dense optical flow correspondence and makes use of a two-view triangulation module to recover an up-to-scale 3D structure. Then, we align the scale of the depth prediction with the triangulated point cloud and use the transformed depth map for depth error computation and dense reprojection check. Our whole system can be jointly trained end-to-end. Extensive experiments show that our system not only reaches state-of-the-art performance on KITTI depth and flow estimation, but also significantly improves the generalization ability of existing self-supervised depth-pose learning methods under a variety of challenging scenarios, and achieves state-of-the-art results among self-supervised learning-based methods on KITTI Odometry and NYUv2 dataset. Furthermore, we present some interesting findings on the limitation of PoseNet-based relative pose estimation methods in terms of generalization ability. Code is available at https://github.com/B1ueber2y/TrianFlow. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhao_Towards_Better_Generalization_Joint_Depth-Pose_Learning_Without_PoseNet_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.01314 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Towards_Better_Generalization_Joint_Depth-Pose_Learning_Without_PoseNet_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Towards_Better_Generalization_Joint_Depth-Pose_Learning_Without_PoseNet_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhao_Towards_Better_Generalization_CVPR_2020_supplemental.pdf | null | null |
Quasi-Newton Solver for Robust Non-Rigid Registration | Yuxin Yao, Bailin Deng, Weiwei Xu, Juyong Zhang | Imperfect data (noise, outliers and partial overlap) and high degrees of freedom make non-rigid registration a classical challenging problem in computer vision. Existing methods typically adopt the l_p type robust estimator to regularize the fitting and smoothness, and the proximal operator is used to solve the resulting non-smooth problem. However, the slow convergence of these algorithms limits its wide applications. In this paper, we propose a formulation for robust non-rigid registration based on a globally smooth robust estimator for data fitting and regularization, which can handle outliers and partial overlaps. We apply the majorization-minimization algorithm to the problem, which reduces each iteration to solving a simple least-squares problem with L-BFGS. Extensive experiments demonstrate the effectiveness of our method for non-rigid alignment between two shapes with outliers and partial overlap. with quantitative evaluation showing that it outperforms state-of-the-art methods in terms of registration accuracy and computational speed. The source code is available at https://github.com/Juyong/Fast_RNRR. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yao_Quasi-Newton_Solver_for_Robust_Non-Rigid_Registration_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.04322 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yao_Quasi-Newton_Solver_for_Robust_Non-Rigid_Registration_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yao_Quasi-Newton_Solver_for_Robust_Non-Rigid_Registration_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yao_Quasi-Newton_Solver_for_CVPR_2020_supplemental.pdf | null | null |
Multi-Scale Progressive Fusion Network for Single Image Deraining | Kui Jiang, Zhongyuan Wang, Peng Yi, Chen Chen, Baojin Huang, Yimin Luo, Jiayi Ma, Junjun Jiang | Rain streaks in the air appear in various blurring degrees and resolutions due to different distances from their positions to the camera. Similar rain patterns are visible in a rain image as well as its multi-scale (or multi-resolution) versions, which makes it possible to exploit such complementary information for rain streak representation. In this work, we explore the multi-scale collaborative representation for rain streaks from the perspective of input image scales and hierarchical deep features in a unified framework, termed multi-scale progressive fusion network (MSPFN) for single image rain streak removal. For the similar rain streaks at different positions, we employ recurrent calculation to capture the global texture, thus allowing to explore the complementary and redundant information at the spatial dimension to characterize target rain streaks. Besides, we construct multi-scale pyramid structure, and further introduce the attention mechanism to guide the fine fusion of these correlated information from different scales. This multi-scale progressive fusion strategy not only promotes the cooperative representation, but also boosts the end-to-end training. Our proposed method is extensively evaluated on several benchmark datasets and achieves the state-of-the-art results. Moreover, we conduct experiments on joint deraining, detection, and segmentation tasks, and inspire a new research direction of vision task driven image deraining. The source code is available at https://github.com/kuihua/MSPFN. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jiang_Multi-Scale_Progressive_Fusion_Network_for_Single_Image_Deraining_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.10985 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_Multi-Scale_Progressive_Fusion_Network_for_Single_Image_Deraining_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_Multi-Scale_Progressive_Fusion_Network_for_Single_Image_Deraining_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Three-Dimensional Reconstruction of Human Interactions | Mihai Fieraru, Mihai Zanfir, Elisabeta Oneata, Alin-Ionut Popa, Vlad Olaru, Cristian Sminchisescu | Understanding 3d human interactions is fundamental for fine grained scene analysis and behavioural modeling. However, most of the existing models focus on analyzing a single person in isolation, and those who process several people focus largely on resolving multi-person data association, rather than inferring interactions. This may lead to incorrect, lifeless 3d estimates, that miss the subtle human contact aspects--the essence of the event--and are of little use for detailed behavioral understanding. This paper addresses such issues and makes several contributions: (1) we introduce models for interaction signature estimation (ISP) encompassing contact detection, segmentation, and 3d contact signature prediction; (2) we show how such components can be leveraged in order to produce augmented losses that ensure contact consistency during 3d reconstruction; (3) we construct several large datasets for learning and evaluating 3d contact prediction and reconstruction methods; specifically, we introduce CHI3D, a lab-based accurate 3d motion capture dataset with 631 sequences containing 2,525 contact events, 728,664 ground truth 3d poses, as well as FlickrCI3D, a dataset of 11,216 images, with 14,081 processed pairs of people, and 81,233 facet-level surface correspondences within 138,213 selected contact regions. Finally, (4) we present models and baselines to illustrate how contact estimation supports meaningful 3d reconstruction where essential interactions are captured. Models and data are made available for research purposes at http://vision.imar.ro/ci3d. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Fieraru_Three-Dimensional_Reconstruction_of_Human_Interactions_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Fieraru_Three-Dimensional_Reconstruction_of_Human_Interactions_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Fieraru_Three-Dimensional_Reconstruction_of_Human_Interactions_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Fieraru_Three-Dimensional_Reconstruction_of_CVPR_2020_supplemental.pdf | null | null |
Real-Time Panoptic Segmentation From Dense Detections | Rui Hou, Jie Li, Arjun Bhargava, Allan Raventos, Vitor Guizilini, Chao Fang, Jerome Lynch, Adrien Gaidon | Panoptic segmentation is a complex full scene parsing task requiring simultaneous instance and semantic segmentation at high resolution. Current state-of-the-art approaches cannot run in real-time, and simplifying these architectures to improve efficiency severely degrades their accuracy. In this paper, we propose a new single-shot panoptic segmentation network that leverages dense detections and a global self-attention mechanism to operate in real-time with performance approaching the state of the art. We introduce a novel parameter-free mask construction method that substantially reduces computational complexity by efficiently reusing information from the object detection and semantic segmentation sub-tasks. The resulting network has a simple data flow that requires no feature map re-sampling, enabling significant hardware acceleration. Our experiments on the Cityscapes and COCO benchmarks show that our network works at 30 FPS on 1024x2048 resolution, trading a 3% relative performance degradation from the current state of the art for up to 440% faster inference. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hou_Real-Time_Panoptic_Segmentation_From_Dense_Detections_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.01202 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hou_Real-Time_Panoptic_Segmentation_From_Dense_Detections_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hou_Real-Time_Panoptic_Segmentation_From_Dense_Detections_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Hou_Real-Time_Panoptic_Segmentation_CVPR_2020_supplemental.zip | null | null |
NestedVAE: Isolating Common Factors via Weak Supervision | Matthew J. Vowels, Necati Cihan Camgoz, Richard Bowden | Fair and unbiased machine learning is an important and active field of research, as decision processes are increasingly driven by models that learn from data. Unfortunately, any biases present in the data may be learned by the model, thereby inappropriately transferring that bias into the decision making process. We identify the connection between the task of bias reduction and that of isolating factors common between domains whilst encouraging domain specific invariance. To isolate the common factors we combine the theory of deep latent variable models with information bottleneck theory for scenarios whereby data may be naturally paired across domains and no additional supervision is required. The result is the Nested Variational AutoEncoder (NestedVAE). Two outer VAEs with shared weights attempt to reconstruct the input and infer a latent space, whilst a nested VAE attempts to reconstruct the latent representation of one image, from the latent representation of its paired image. In so doing, the nested VAE isolates the common latent factors/causes and becomes invariant to unwanted factors that are not shared between paired images. We also propose a new metric to provide a balanced method of evaluating consistency and classifier performance across domains which we refer to as the Adjusted Parity metric. An evaluation of NestedVAE on both domain and attribute invariance, change detection, and learning common factors for the prediction of biological sex demonstrates that NestedVAE significantly outperforms alternative methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Vowels_NestedVAE_Isolating_Common_Factors_via_Weak_Supervision_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.11576 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Vowels_NestedVAE_Isolating_Common_Factors_via_Weak_Supervision_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Vowels_NestedVAE_Isolating_Common_Factors_via_Weak_Supervision_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Vowels_NestedVAE_Isolating_Common_CVPR_2020_supplemental.pdf | null | null |
Recurrent Feature Reasoning for Image Inpainting | Jingyuan Li, Ning Wang, Lefei Zhang, Bo Du, Dacheng Tao | Existing inpainting methods have achieved promising performance for recovering regular or small image defects. However, filling in large continuous holes remains difficult due to the lack of constraints for the hole center. In this paper, we devise a Recurrent Feature Reasoning (RFR) network which is mainly constructed by a plug-and-play Recurrent Feature Reasoning module and a Knowledge Consistent Attention (KCA) module. Analogous to how humans solve puzzles (i.e., first solve the easier parts and then use the results as additional information to solve difficult parts), the RFR module recurrently infers the hole boundaries of the convolutional feature maps and then uses them as clues for further inference. The module progressively strengthens the constraints for the hole center and the results become explicit. To capture information from distant places in the feature map for RFR, we further develop KCA and incorporate it in RFR. Empirically, we first compare the proposed RFR-Net with existing backbones, demonstrating that RFR-Net is more efficient (e.g., a 4% SSIM improvement for the same model size). We then place the network in the context of the current state-of-the-art, where it exhibits improved performance. The corresponding source code is available at: https://github.com/jingyuanli001/RFR-Inpainting | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Recurrent_Feature_Reasoning_for_Image_Inpainting_CVPR_2020_paper.pdf | http://arxiv.org/abs/2008.03737 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Recurrent_Feature_Reasoning_for_Image_Inpainting_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Recurrent_Feature_Reasoning_for_Image_Inpainting_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Recurrent_Feature_Reasoning_CVPR_2020_supplemental.pdf | null | null |
Harmonizing Transferability and Discriminability for Adapting Object Detectors | Chaoqi Chen, Zebiao Zheng, Xinghao Ding, Yue Huang, Qi Dou | Recent advances in adaptive object detection have achieved compelling results in virtue of adversarial feature adaptation to mitigate the distributional shifts along the detection pipeline. Whilst adversarial adaptation significantly enhances the transferability of feature representations, the feature discriminability of object detectors remains less investigated. Moreover, transferability and discriminability may come at a contradiction in adversarial adaptation given the complex combinations of objects and the differentiated scene layouts between domains. In this paper, we propose a Hierarchical Transferability Calibration Network (HTCN) that hierarchically (local-region/image/instance) calibrates the transferability of feature representations for harmonizing transferability and discriminability. The proposed model consists of three components: (1) Importance Weighted Adversarial Training with input Interpolation (IWAT-I), which strengthens the global discriminability by re-weighting the interpolated image-level features; (2) Context-aware Instance-Level Alignment (CILA) module, which enhances the local discriminability by capturing the underlying complementary effect between the instance-level feature and the global context information for the instance-level feature alignment; (3) local feature masks that calibrate the local transferability to provide semantic guidance for the following discriminative pattern alignment. Experimental results show that HTCN significantly outperforms the state-of-the-art methods on benchmark datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Harmonizing_Transferability_and_Discriminability_for_Adapting_Object_Detectors_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.06297 | https://www.youtube.com/watch?v=N1Xa6h5-9LA | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Harmonizing_Transferability_and_Discriminability_for_Adapting_Object_Detectors_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Harmonizing_Transferability_and_Discriminability_for_Adapting_Object_Detectors_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_Harmonizing_Transferability_and_CVPR_2020_supplemental.pdf | null | null |
Unsupervised Magnification of Posture Deviations Across Subjects | Michael Dorkenwald, Uta Buchler, Bjorn Ommer | Analyzing human posture and precisely comparing it across different subjects is essential for accurate understanding of behavior and numerous vision applications such as medical diagnostics, sports, or surveillance. Motion magnification techniques help to see even small deviations in posture that are invisible to the naked eye. However, they fail when comparing subtle posture differences across individuals with diverse appearance. Keypoint-based posture estimation and classification techniques can handle large variations in appearance, but are invariant to subtle deviations in posture. We present an approach to unsupervised magnification of posture differences across individuals despite large deviations in appearance. We do not require keypoint annotation and visualize deviations on a sub-bodypart level. To transfer appearance across subjects onto a magnified posture, we propose a novel loss for disentangling appearance and posture in an autoencoder. Posture magnification yields exaggerated images that are different from the training set. Therefore, we incorporate magnification already into the training of the disentangled autoencoder and learn on real data and synthesized magnifications without supervision. Experiments confirm that our approach improves upon the state-of-the-art in magnification and on the application of discovering posture deviations due to impairment. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Dorkenwald_Unsupervised_Magnification_of_Posture_Deviations_Across_Subjects_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Dorkenwald_Unsupervised_Magnification_of_Posture_Deviations_Across_Subjects_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Dorkenwald_Unsupervised_Magnification_of_Posture_Deviations_Across_Subjects_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Dorkenwald_Unsupervised_Magnification_of_CVPR_2020_supplemental.zip | null | null |
PADS: Policy-Adapted Sampling for Visual Similarity Learning | Karsten Roth, Timo Milbich, Bjorn Ommer | Learning visual similarity requires to learn relations, typically between triplets of images. Albeit triplet approaches being powerful, their computational complexity mostly limits training to only a subset of all possible training triplets. Thus, sampling strategies that decide when to use which training sample during learning are crucial. Currently, the prominent paradigm are fixed or curriculum sampling strategies that are predefined before training starts. However, the problem truly calls for a sampling process that adjusts based on the actual state of the similarity representation during training. We, therefore, employ reinforcement learning and have a teacher network adjust the sampling distribution based on the current state of the learner network, which represents visual similarity. Experiments on benchmark datasets using standard triplet-based losses show that our adaptive sampling strategy significantly outperforms fixed sampling strategies. Moreover, although our adaptive sampling is only applied on top of basic triplet-learning frameworks, we reach competitive results to state-of-the-art approaches that employ diverse additional learning signals or strong ensemble architectures. Code can be found under https://github.com/Confusezius/CVPR2020_PADS. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Roth_PADS_Policy-Adapted_Sampling_for_Visual_Similarity_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.11113 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Roth_PADS_Policy-Adapted_Sampling_for_Visual_Similarity_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Roth_PADS_Policy-Adapted_Sampling_for_Visual_Similarity_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Roth_PADS_Policy-Adapted_Sampling_CVPR_2020_supplemental.pdf | null | null |
Interactive Multi-Label CNN Learning With Partial Labels | Dat Huynh, Ehsan Elhamifar | We address the problem of efficient end-to-end learning a multi-label Convolutional Neural Network (CNN) on training images with partial labels. Training a CNN with partial labels, hence a small number of images for every label, using the standard cross-entropy loss is prone to overfitting and performance drop. We introduce a new loss function that regularizes the cross-entropy loss with a cost function that measures the smoothness of labels and features of images on the data manifold. Given that optimizing the new loss function over the CNN parameters requires learning similarities among labels and images, which itself depends on knowing the parameters of the CNN, we develop an efficient interactive learning framework in which the two steps of similarity learning and CNN training interact and improve the performance of each another. Our method learns the CNN parameters without requiring keeping all training data in the memory, allows to learn few informative similarities only for images in each mini-batch and handles changing feature representations. By extensive experiments on Open Images, CUB and MS-COCO datasets,we demonstrate the effectiveness of our method. In particular, on the large-scale Open Images dataset, we improve the state of the art by 1.02% in mAP score over 5,000 classes. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Huynh_Interactive_Multi-Label_CNN_Learning_With_Partial_Labels_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Huynh_Interactive_Multi-Label_CNN_Learning_With_Partial_Labels_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Huynh_Interactive_Multi-Label_CNN_Learning_With_Partial_Labels_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Huynh_Interactive_Multi-Label_CNN_CVPR_2020_supplemental.pdf | null | null |
SketchyCOCO: Image Generation From Freehand Scene Sketches | Chengying Gao, Qi Liu, Qi Xu, Limin Wang, Jianzhuang Liu, Changqing Zou | We introduce the first method for automatic image generation from scene-level freehand sketches. Our model allows for controllable image generation by specifying the synthesis goal via freehand sketches. The key contribution is an attribute vector bridged Generative Adversarial Network called EdgeGAN, which supports high visual-quality object-level image content generation without using freehand sketches as training data. We have built a large-scale composite dataset called SketchyCOCO to support and evaluate the solution. We validate our approach on the tasks of both object-level and scene-level image generation on SketchyCOCO. Through quantitative, qualitative results, human evaluation and ablation studies, we demonstrate the method's capacity to generate realistic complex scene-level images from various freehand sketches. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gao_SketchyCOCO_Image_Generation_From_Freehand_Scene_Sketches_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.02683 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_SketchyCOCO_Image_Generation_From_Freehand_Scene_Sketches_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_SketchyCOCO_Image_Generation_From_Freehand_Scene_Sketches_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Gao_SketchyCOCO_Image_Generation_CVPR_2020_supplemental.pdf | null | null |
Effectively Unbiased FID and Inception Score and Where to Find Them | Min Jin Chong, David Forsyth | This paper shows that two commonly used evaluation metrics for generative models, the Frechet Inception Distance (FID) and the Inception Score (IS), are biased -- the expected value of the score computed for a finite sample set is not the true value of the score. Worse, the paper shows that the bias term depends on the particular model being evaluated, so model A may get a better score than model B simply because model A's bias term is smaller. This effect cannot be fixed by evaluating at a fixed number of samples. This means all comparisons using FID or IS as currently computed are unreliable. We then show how to extrapolate the score to obtain an effectively bias-free estimate of scores computed with an infinite number of samples, which we term FID Infinity and IS Infinity. In turn, this effectively bias-free estimate requires good estimates of scores with a finite number of samples. We show that using Quasi-Monte Carlo integration notably improves estimates of FID and IS for finite sample sets. Our extrapolated scores are simple, drop-in replacements for the finite sample scores. Additionally, we show that using low discrepancy sequence in GAN training offers small improvements in the resulting generator. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chong_Effectively_Unbiased_FID_and_Inception_Score_and_Where_to_Find_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.07023 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chong_Effectively_Unbiased_FID_and_Inception_Score_and_Where_to_Find_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chong_Effectively_Unbiased_FID_and_Inception_Score_and_Where_to_Find_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chong_Effectively_Unbiased_FID_CVPR_2020_supplemental.pdf | null | null |
Controllable Orthogonalization in Training DNNs | Lei Huang, Li Liu, Fan Zhu, Diwen Wan, Zehuan Yuan, Bo Li, Ling Shao | Orthogonality is widely used for training deep neural networks (DNNs) due to its ability to maintain all singular values of the Jacobian close to 1 and reduce redundancy in representation. This paper proposes a computationally efficient and numerically stable orthogonalization method using Newton's iteration (ONI), to learn a layer-wise orthogonal weight matrix in DNNs. ONI works by iteratively stretching the singular values of a weight matrix towards 1. This property enables it to control the orthogonality of a weight matrix by its number of iterations. We show that our method improves the performance of image classification networks by effectively controlling the orthogonality to provide an optimal tradeoff between optimization benefits and representational capacity reduction. We also show that ONI stabilizes the training of generative adversarial networks (GANs) by maintaining the Lipschitz continuity of a network, similar to spectral normalization (SN), and further outperforms SN by providing controllable orthogonality. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Huang_Controllable_Orthogonalization_in_Training_DNNs_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00917 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Controllable_Orthogonalization_in_Training_DNNs_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Controllable_Orthogonalization_in_Training_DNNs_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Huang_Controllable_Orthogonalization_in_CVPR_2020_supplemental.pdf | null | null |
Interpreting the Latent Space of GANs for Semantic Face Editing | Yujun Shen, Jinjin Gu, Xiaoou Tang, Bolei Zhou | Despite the recent advance of Generative Adversarial Networks (GANs) in high-fidelity image synthesis, there lacks enough understanding of how GANs are able to map a latent code sampled from a random distribution to a photo-realistic image. Previous work assumes the latent space learned by GANs follows a distributed representation but observes the vector arithmetic phenomenon. In this work, we propose a novel framework, called InterFaceGAN, for semantic face editing by interpreting the latent semantics learned by GANs. In this framework, we conduct a detailed study on how different semantics are encoded in the latent space of GANs for face synthesis. We find that the latent code of well-trained generative models actually learns a disentangled representation after linear transformations. We explore the disentanglement between various semantics and manage to decouple some entangled semantics with subspace projection, leading to more precise control of facial attributes. Besides manipulating gender, age, expression, and the presence of eyeglasses, we can even vary the face pose as well as fix the artifacts accidentally generated by GAN models. The proposed method is further applied to achieve real image manipulation when combined with GAN inversion methods or some encoder-involved models. Extensive results suggest that learning to synthesize faces spontaneously brings a disentangled and controllable facial attribute representation. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shen_Interpreting_the_Latent_Space_of_GANs_for_Semantic_Face_Editing_CVPR_2020_paper.pdf | http://arxiv.org/abs/1907.10786 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Shen_Interpreting_the_Latent_Space_of_GANs_for_Semantic_Face_Editing_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Shen_Interpreting_the_Latent_Space_of_GANs_for_Semantic_Face_Editing_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Shen_Interpreting_the_Latent_CVPR_2020_supplemental.pdf | null | null |
Tracking by Instance Detection: A Meta-Learning Approach | Guangting Wang, Chong Luo, Xiaoyan Sun, Zhiwei Xiong, Wenjun Zeng | We consider the tracking problem as a special type of object detection problem, which we call instance detection. With proper initialization, a detector can be quickly converted into a tracker by learning the new instance from a single image. We find that model-agnostic meta-learning (MAML) offers a strategy to initialize the detector that satisfies our needs. We propose a principled three-step approach to build a high-performance tracker. First, pick any modern object detector trained with gradient descent. Second, conduct offline training (or initialization) with MAML. Third, perform domain adaptation using the initial frame. We follow this procedure to build two trackers, named Retina-MAML and FCOS-MAML, based on two modern detectors RetinaNet and FCOS. Evaluations on four benchmarks show that both trackers are competitive against state-of-the-art trackers. On OTB-100, Retina-MAML achieves the highest ever AUC of 0.712. On TrackingNet, FCOS-MAML ranks the first on the leader board with an AUC of 0.757 and the normalized precision of 0.822. Both trackers run in real-time at 40 FPS. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Tracking_by_Instance_Detection_A_Meta-Learning_Approach_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00830 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Tracking_by_Instance_Detection_A_Meta-Learning_Approach_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Tracking_by_Instance_Detection_A_Meta-Learning_Approach_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learned Image Compression With Discretized Gaussian Mixture Likelihoods and Attention Modules | Zhengxue Cheng, Heming Sun, Masaru Takeuchi, Jiro Katto | Image compression is a fundamental research field and many well-known compression standards have been developed for many decades. Recently, learned compression methods exhibit a fast development trend with promising results. However, there is still a performance gap between learned compression algorithms and reigning compression standards, especially in terms of widely used PSNR metric. In this paper, we explore the remaining redundancy of recent learned compression algorithms. We have found accurate entropy models for rate estimation largely affect the optimization of network parameters and thus affect the rate-distortion performance. Therefore, in this paper, we propose to use discretized Gaussian Mixture Likelihoods to parameterize the distributions of latent codes, which can achieve a more accurate and flexible entropy model. Besides, we take advantage of recent attention modules and incorporate them into network architecture to enhance the performance. Experimental results demonstrate our proposed method achieves a state-of-the-art performance compared to existing learned compression methods on both Kodak and high-resolution datasets. To our knowledge our approach is the first work to achieve comparable performance with latest compression standard Versatile Video Coding (VVC) regarding PSNR. More importantly, our approach generates more visually pleasant results when optimized by MS-SSIM. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cheng_Learned_Image_Compression_With_Discretized_Gaussian_Mixture_Likelihoods_and_Attention_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.01568 | https://www.youtube.com/watch?v=RGeDlwBFxUY | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cheng_Learned_Image_Compression_With_Discretized_Gaussian_Mixture_Likelihoods_and_Attention_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cheng_Learned_Image_Compression_With_Discretized_Gaussian_Mixture_Likelihoods_and_Attention_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Cheng_Learned_Image_Compression_CVPR_2020_supplemental.pdf | null | null |
PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation | Li Jiang, Hengshuang Zhao, Shaoshuai Shi, Shu Liu, Chi-Wing Fu, Jiaya Jia | Instance segmentation is an important task for scene understanding. Compared to the fully-developed 2D, 3D instance segmentation for point clouds have much room to improve. In this paper, we present PointGroup, a new end-to-end bottom-up architecture, specifically focused on better grouping the points by exploring the void space between objects. We design a two-branch network to extract point features and predict semantic labels and offsets, for shifting each point towards its respective instance centroid. A clustering component is followed to utilize both the original and offset-shifted point coordinate sets, taking advantage of their complementary strength. Further, we formulate the ScoreNet to evaluate the candidate instances, followed by the Non-Maximum Suppression (NMS) to remove duplicates. We conduct extensive experiments on two challenging datasets, ScanNet v2 and S3DIS, on which our method achieves the highest performance, 63.6% and 64.0%, compared to 54.9% and 54.4% achieved by former best solutions in terms of mAP with IoU threshold 0.5. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jiang_PointGroup_Dual-Set_Point_Grouping_for_3D_Instance_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.01658 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_PointGroup_Dual-Set_Point_Grouping_for_3D_Instance_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_PointGroup_Dual-Set_Point_Grouping_for_3D_Instance_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Semantic Drift Compensation for Class-Incremental Learning | Lu Yu, Bartlomiej Twardowski, Xialei Liu, Luis Herranz, Kai Wang, Yongmei Cheng, Shangling Jui, Joost van de Weijer | Class-incremental learning of deep networks sequentially increases the number of classes to be classified. During training, the network has only access to data of one task at a time, where each task contains several classes. In this setting, networks suffer from catastrophic forgetting which refers to the drastic drop in performance on previous tasks. The vast majority of methods have studied this scenario for classification networks, where for each new task the classification layer of the network must be augmented with additional weights to make room for the newly added classes. Embedding networks have the advantage that new classes can be naturally included into the network without adding new weights. Therefore, we study incremental learning for embedding networks. In addition, we propose a new method to estimate the drift, called semantic drift, of features and compensate for it without the need of any exemplars. We approximate the drift of previous tasks based on the drift that is experienced by current task data. We perform experiments on fine-grained datasets, CIFAR100 and ImageNet-Subset. We demonstrate that embedding networks suffer significantly less from catastrophic forgetting. We outperform existing methods which do not require exemplars and obtain competitive results compared to methods which store exemplars. Furthermore, we show that our proposed SDC when combined with existing methods to prevent forgetting consistently improves results. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yu_Semantic_Drift_Compensation_for_Class-Incremental_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00440 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_Semantic_Drift_Compensation_for_Class-Incremental_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_Semantic_Drift_Compensation_for_Class-Incremental_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yu_Semantic_Drift_Compensation_CVPR_2020_supplemental.pdf | null | null |
Generating 3D People in Scenes Without People | Yan Zhang, Mohamed Hassan, Heiko Neumann, Michael J. Black, Siyu Tang | We present a fully automatic system that takes a 3D scene and generates plausible 3D human bodies that are posed naturally in that 3D scene. Given a 3D scene without people, humans can easily imagine how people could interact with the scene and the objects in it. However, this is a challenging task for a computer as solving it requires that (1) the generated human bodies to be semantically plausible within the 3D environment (e.g. people sitting on the sofa or cooking near the stove), and (2) the generated human-scene interaction to be physically feasible such that the human body and scene do not interpenetrate while, at the same time, body-scene contact supports physical interactions. To that end, we make use of the surface-based 3D human model SMPL-X. We first train a conditional variational autoencoder to predict semantically plausible 3D human poses conditioned on latent scene representations, then we further refine the generated 3D bodies using scene constraints to enforce feasible physical interaction. We show that our approach is able to synthesize realistic and expressive 3D human bodies that naturally interact with 3D environment. We perform extensive experiments demonstrating that our generative framework compares favorably with existing methods, both qualitatively and quantitatively. We believe that our scene-conditioned 3D human generation pipeline will be useful for numerous applications; e.g. to generate training data for human pose estimation, in video games and in VR/AR. Our project page for data and code can be seen at: https://vlg.inf.ethz.ch/projects/PSI/ . | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Generating_3D_People_in_Scenes_Without_People_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.02923 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Generating_3D_People_in_Scenes_Without_People_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Generating_3D_People_in_Scenes_Without_People_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhang_Generating_3D_People_CVPR_2020_supplemental.zip | null | null |
Computing Valid P-Values for Image Segmentation by Selective Inference | Kosuke Tanizaki, Noriaki Hashimoto, Yu Inatsu, Hidekata Hontani, Ichiro Takeuchi | Image segmentation is one of the most fundamental tasks in computer vision. In many practical applications, it is essential to properly evaluate the reliability of individual segmentation results. In this study, we propose a novel framework for quantifying the statistical significance of individual segmentation results in the form of p-values by statistically testing the difference between the object region and the background region. This seemingly simple problem is actually quite challenging because the difference --- called segmentation bias --- can be deceptively large due to the adaptation of the segmentation algorithm to the data. To overcome this difficulty, we introduce a statistical approach called selective inference, and develop a framework for computing valid p-values in which segmentation bias is properly accounted for. Although the proposed framework is potentially applicable to various segmentation algorithms, we focus in this paper on graph-cut- and threshold-based segmentation algorithms, and develop two specific methods for computing valid p-values for the segmentation results obtained by these algorithms. We prove the theoretical validity of these two methods and demonstrate their practicality by applying them to the segmentation of medical images. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tanizaki_Computing_Valid_P-Values_for_Image_Segmentation_by_Selective_Inference_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tanizaki_Computing_Valid_P-Values_for_Image_Segmentation_by_Selective_Inference_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tanizaki_Computing_Valid_P-Values_for_Image_Segmentation_by_Selective_Inference_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Tanizaki_Computing_Valid_P-Values_CVPR_2020_supplemental.pdf | null | null |
Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking | Jin Gao, Weiming Hu, Yan Lu | Online learning is crucial to robust visual object tracking as it can provide high discrimination power in the presence of background distractors. However, there are two contradictory factors affecting its successful deployment on the real visual tracking platform: the discrimination issue due to the challenges in vanilla gradient descent, which does not guarantee good convergence; the robustness issue due to over-fitting resulting from excessive update with limited memory size (the oldest samples are discarded). Despite many dedicated techniques proposed to somehow treat those issues, in this paper we take a new way to strike a compromise between them based on the recursive least-squares estimation (LSE) algorithm. After connecting each fully-connected layer with LSE separately via normal equations, we further propose an improved mini-batch stochastic gradient descent algorithm for fully-connected network learning with memory retention in a recursive fashion. This characteristic can spontaneously reduce the risk of over-fitting resulting from catastrophic forgetting in excessive online learning. Meanwhile, it can effectively improve convergence though the cost function is computed over all the training samples that the algorithm has ever seen. We realize this recursive LSE-aided online learning technique in the state-of-the-art RT-MDNet tracker, and the consistent improvements on four challenging benchmarks prove its efficiency without additional offline training and too much tedious work on parameter adjusting. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gao_Recursive_Least-Squares_Estimator-Aided_Online_Learning_for_Visual_Tracking_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=bUGbO7caPWE | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_Recursive_Least-Squares_Estimator-Aided_Online_Learning_for_Visual_Tracking_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_Recursive_Least-Squares_Estimator-Aided_Online_Learning_for_Visual_Tracking_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Gao_Recursive_Least-Squares_Estimator-Aided_CVPR_2020_supplemental.pdf | null | null |
End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection | Rui Qian, Divyansh Garg, Yan Wang, Yurong You, Serge Belongie, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger, Wei-Lun Chao | Reliable and accurate 3D object detection is a necessity for safe autonomous driving. Although LiDAR sensors can provide accurate 3D point cloud estimates of the environment, they are also prohibitively expensive for many settings. Recently, the introduction of pseudo-LiDAR (PL) has led to a drastic reduction in the accuracy gap between methods based on LiDAR sensors and those based on cheap stereo cameras. PL combines state-of-the-art deep neural networks for 3D depth estimation with those for 3D object detection by converting 2D depth map outputs to 3D point cloud inputs. However, so far these two networks have to be trained separately. In this paper, we introduce a new framework based on differentiable Change of Representation (CoR) modules that allow the entire PL pipeline to be trained end-to-end. The resulting framework is compatible with most state-of-the-art networks for both tasks and in combination with PointRCNN improves over PL consistently across all benchmarks --- yielding the highest entry on the KITTI image-based 3D object detection leaderboard at the time of submission. Our code will be made available at https://github.com/mileyan/pseudo-LiDAR_e2e. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Qian_End-to-End_Pseudo-LiDAR_for_Image-Based_3D_Object_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.03080 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Qian_End-to-End_Pseudo-LiDAR_for_Image-Based_3D_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Qian_End-to-End_Pseudo-LiDAR_for_Image-Based_3D_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Qian_End-to-End_Pseudo-LiDAR_for_CVPR_2020_supplemental.pdf | null | null |
A Quantum Computational Approach to Correspondence Problems on Point Sets | Vladislav Golyanik, Christian Theobalt | Modern adiabatic quantum computers (AQC) are already used to solve difficult combinatorial optimisation problems in various domains of science. Currently, only a few applications of AQC in computer vision have been demonstrated. We review AQC and derive a new algorithm for correspondence problems on point sets suitable for execution on AQC. Our algorithm has a subquadratic computational complexity of the state preparation. Examples of successful transformation estimation and point set alignment by simulated sampling are shown in the systematic experimental evaluation. Finally, we analyse the differences in the solutions and the corresponding energy values. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Golyanik_A_Quantum_Computational_Approach_to_Correspondence_Problems_on_Point_Sets_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.12296 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Golyanik_A_Quantum_Computational_Approach_to_Correspondence_Problems_on_Point_Sets_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Golyanik_A_Quantum_Computational_Approach_to_Correspondence_Problems_on_Point_Sets_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Golyanik_A_Quantum_Computational_CVPR_2020_supplemental.pdf | null | null |
High-Frequency Component Helps Explain the Generalization of Convolutional Neural Networks | Haohan Wang, Xindi Wu, Zeyi Huang, Eric P. Xing | We investigate the relationship between the frequency spectrum of image data and the generalization behavior of convolutional neural networks (CNN). We first notice CNN's ability in capturing the high-frequency components of images. These high-frequency components are almost imperceptible to a human. Thus the observation leads to multiple hypotheses that are related to the generalization behaviors of CNN, including a potential explanation for adversarial examples, a discussion of CNN's trade-off between robustness and accuracy, and some evidence in understanding training heuristics. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_High-Frequency_Component_Helps_Explain_the_Generalization_of_Convolutional_Neural_Networks_CVPR_2020_paper.pdf | http://arxiv.org/abs/1905.13545 | https://www.youtube.com/watch?v=8H0QQbMFb-k | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_High-Frequency_Component_Helps_Explain_the_Generalization_of_Convolutional_Neural_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_High-Frequency_Component_Helps_Explain_the_Generalization_of_Convolutional_Neural_Networks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_High-Frequency_Component_Helps_CVPR_2020_supplemental.pdf | null | null |
Rotate-and-Render: Unsupervised Photorealistic Face Rotation From Single-View Images | Hang Zhou, Jihao Liu, Ziwei Liu, Yu Liu, Xiaogang Wang | Though face rotation has achieved rapid progress in recent years, the lack of high-quality paired training data remains a great hurdle for existing methods. The current generative models heavily rely on datasets with multi-view images of the same person. Thus, their generated results are restricted by the scale and domain of the data source. To overcome these challenges, we propose a novel unsupervised framework that can synthesize photo-realistic rotated faces using only single-view image collections in the wild. Our key insight is that rotating faces in the 3D space back and forth, and re-rendering them to the 2D plane can serve as a strong self-supervision. We leverage the recent advances in 3D face modeling and high-resolution GAN to constitute our building blocks. Since the 3D rotation-and-render on faces can be applied to arbitrary angles without losing details, our approach is extremely suitable for in-the-wild scenarios (i.e. no paired data are available), where existing methods fall short. Extensive experiments demonstrate that our approach has superior synthesis quality as well as identity preservation over the state-of-the-art methods, across a wide range of poses and domains. Furthermore, we validate that our rotate-and-render framework naturally can act as an effective data augmentation engine for boosting modern face recognition systems even on strong baseline models | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhou_Rotate-and-Render_Unsupervised_Photorealistic_Face_Rotation_From_Single-View_Images_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=IAh5MDJADpU | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Rotate-and-Render_Unsupervised_Photorealistic_Face_Rotation_From_Single-View_Images_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Rotate-and-Render_Unsupervised_Photorealistic_Face_Rotation_From_Single-View_Images_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhou_Rotate-and-Render_Unsupervised_Photorealistic_CVPR_2020_supplemental.pdf | null | null |
Scene-Adaptive Video Frame Interpolation via Meta-Learning | Myungsub Choi, Janghoon Choi, Sungyong Baik, Tae Hyun Kim, Kyoung Mu Lee | Video frame interpolation is a challenging problem because there are different scenarios for each video depending on the variety of foreground and background motion, frame rate, and occlusion. It is therefore difficult for a single network with fixed parameters to generalize across different videos. Ideally, one could have a different network for each scenario, but this is computationally infeasible for practical applications. In this work, we propose to adapt the model to each video by making use of additional information that is readily available at test time and yet has not been exploited in previous works. We first show the benefits of 'test-time adaptation' through simple fine-tuning of a network, then we greatly improve its efficiency by incorporating meta-learning. We obtain significant performance gains with only a single gradient update without any additional parameters. Finally, we show that our meta-learning framework can be easily employed to any video frame interpolation network and can consistently improve its performance on multiple benchmark datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Choi_Scene-Adaptive_Video_Frame_Interpolation_via_Meta-Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00779 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Choi_Scene-Adaptive_Video_Frame_Interpolation_via_Meta-Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Choi_Scene-Adaptive_Video_Frame_Interpolation_via_Meta-Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Choi_Scene-Adaptive_Video_Frame_CVPR_2020_supplemental.zip | null | null |
On the Distribution of Minima in Intrinsic-Metric Rotation Averaging | Kyle Wilson, David Bindel | Rotation Averaging is a non-convex optimization problem that determines orientations of a collection of cameras from their images of a 3D scene. The problem has been studied using a variety of distances and robustifiers. The intrinsic (or geodesic) distance on SO(3) is geometrically meaningful; but while some extrinsic distance-based solvers admit (conditional) guarantees of correctness, no comparable results have been found under the intrinsic metric. In this paper, we study the spatial distribution of local minima. First, we do a novel empirical study to demonstrate sharp transitions in qualitative behavior: as problems become noisier, they transition from a single (easy-to-find) dominant minimum to a cost surface filled with minima. In the second part of this paper we derive a theoretical bound for when this transition occurs. This is an extension of the results of [24], which used local convexity as a proxy to study the difficulty of problem. By recognizing the underly- ing quotient manifold geometry of the problem we achieve an n-fold improvement over prior work. Incidentally, our analysis also extends the prior l2 work to general lp costs. Our results suggest using algebraic connectivity as an indicator of problem difficulty. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wilson_On_the_Distribution_of_Minima_in_Intrinsic-Metric_Rotation_Averaging_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.08310 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wilson_On_the_Distribution_of_Minima_in_Intrinsic-Metric_Rotation_Averaging_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wilson_On_the_Distribution_of_Minima_in_Intrinsic-Metric_Rotation_Averaging_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wilson_On_the_Distribution_CVPR_2020_supplemental.pdf | null | null |
Explainable Object-Induced Action Decision for Autonomous Vehicles | Yiran Xu, Xiaoyin Yang, Lihang Gong, Hsuan-Chu Lin, Tz-Ying Wu, Yunsheng Li, Nuno Vasconcelos | A new paradigm is proposed for autonomous driving. The new paradigm lies between the end-to-end and pipelined approaches, and is inspired by how humans solve the problem. While it relies on scene understanding, the latter only considers objects that could originate hazard. These are denoted as action inducing, since changes in their state should trigger vehicle actions. They also define a set of explanations for these actions, which should be produced jointly with the latter. An extension of the BDD100K dataset, annotated for a set of 4 actions and 21 explanations, is proposed. A new multi-task formulation of the problem, which optimizes the accuracy of both action commands and explanations, is then introduced. A CNN architecture is finally proposed to solve this problem, by combining reasoning about action inducing objects and global scene context. Experimental results show that the requirement of explanations improves the recognition of action-inducing objects, which in turn leads to better action predictions. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xu_Explainable_Object-Induced_Action_Decision_for_Autonomous_Vehicles_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.09405 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Explainable_Object-Induced_Action_Decision_for_Autonomous_Vehicles_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Explainable_Object-Induced_Action_Decision_for_Autonomous_Vehicles_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
DLWL: Improving Detection for Lowshot Classes With Weakly Labelled Data | Vignesh Ramanathan, Rui Wang, Dhruv Mahajan | Large detection datasets have a long tail of lowshot classes with very few bounding box annotations. We wish to improve detection for lowshot classes with weakly labelled web-scale datasets only having image-level labels. This requires a detection framework that can be jointly trained with limited number of bounding box annotated images and large number of weakly labelled images. Towards this end, we propose a modification to the FRCNN model to automatically infer label assignment for objects proposals from weakly labelled images during training. We pose this label assignment as a Linear Program with constraints on the number and overlap of object instances in an image. We show that this can be solved efficiently during training for weakly labelled images. Compared to just training with few annotated examples, augmenting with weakly labelled examples in our framework provides significant gains. We demonstrate this on the LVIS dataset 3.5 gain in AP as well as different lowshot variants of the COCO dataset. We provide a thorough analysis of the effect of amount of weakly labelled and fully labelled data required to train the detection model. Our DLWL framework can also outperform self-supervised baselines like omni-supervision for lowshot classes. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ramanathan_DLWL_Improving_Detection_for_Lowshot_Classes_With_Weakly_Labelled_Data_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ramanathan_DLWL_Improving_Detection_for_Lowshot_Classes_With_Weakly_Labelled_Data_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ramanathan_DLWL_Improving_Detection_for_Lowshot_Classes_With_Weakly_Labelled_Data_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Ramanathan_DLWL_Improving_Detection_CVPR_2020_supplemental.pdf | null | null |
Robust Partial Matching for Person Search in the Wild | Yingji Zhong, Xiaoyu Wang, Shiliang Zhang | Various factors like occlusions, backgrounds, etc., would lead to misaligned detected bounding boxes , e.g., ones covering only portions of human body. This issue is common but overlooked by previous person search works. To alleviate this issue, this paper proposes an Align-to-Part Network (APNet) for person detection and re-Identification (reID). APNet refines detected bounding boxes to cover the estimated holistic body regions, from which discriminative part features can be extracted and aligned. Aligned part features naturally formulate reID as a partial feature matching procedure, where valid part features are selected for similarity computation, while part features on occluded or noisy regions are discarded. This design enhances the robustness of person search to real-world challenges with marginal computation overhead. This paper also contributes a Large-Scale dataset for Person Search in the wild (LSPS), which is by far the largest and the most challenging dataset for person search. Experiments show that APNet brings considerable performance improvement on LSPS. Meanwhile, it achieves competitive performance on existing person search benchmarks like CUHK-SYSU and PRW. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhong_Robust_Partial_Matching_for_Person_Search_in_the_Wild_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.09329 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhong_Robust_Partial_Matching_for_Person_Search_in_the_Wild_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhong_Robust_Partial_Matching_for_Person_Search_in_the_Wild_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Watch Your Up-Convolution: CNN Based Generative Deep Neural Networks Are Failing to Reproduce Spectral Distributions | Ricard Durall, Margret Keuper, Janis Keuper | Generative convolutional deep neural networks, e.g. popular GAN architectures, are relying on convolution based up-sampling methods to produce non-scalar outputs like images or video sequences. In this paper, we show that common up-sampling methods, i.e. known as up-convolution or transposed convolution, are causing the inability of such models to reproduce spectral distributions of natural training data correctly. This effect is independent of the underlying architecture and we show that it can be used to easily detect generated data like deepfakes with up to 100% accuracy on public benchmarks. To overcome this drawback of current generative models, we propose to add a novel spectral regularization term to the training optimization objective. We show that this approach not only allows to train spectral consistent GANs that are avoiding high frequency errors. Also, we show that a correct approximation of the frequency spectrum has positive effects on the training stability and output quality of generative networks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Durall_Watch_Your_Up-Convolution_CNN_Based_Generative_Deep_Neural_Networks_Are_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=rlpqO6v9J8k | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Durall_Watch_Your_Up-Convolution_CNN_Based_Generative_Deep_Neural_Networks_Are_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Durall_Watch_Your_Up-Convolution_CNN_Based_Generative_Deep_Neural_Networks_Are_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Durall_Watch_Your_Up-Convolution_CVPR_2020_supplemental.pdf | null | null |
The Devil Is in the Details: Delving Into Unbiased Data Processing for Human Pose Estimation | Junjie Huang, Zheng Zhu, Feng Guo, Guan Huang | Recently, the leading performance of human pose estimation is dominated by top-down methods. Being a fundamental component in training and inference, data processing has not been systematically considered in pose estimation community, to the best of our knowledge. In this paper, we focus on this problem and find that the devil of top-down pose estimator is in the biased data processing. Specifically, by investigating the standard data processing in state-of-the-art approaches mainly including data transformation and encoding-decoding, we find that the results obtained by common flipping strategy are unaligned with the original ones in inference. Moreover, there is statistical error in standard encoding-decoding during both training and inference. Two problems couple together and significantly degrade the pose estimation performance. Based on quantitative analyses, we then formulate a principled way to tackle this dilemma. Data is processed in continuous space based on unit length (the intervals between pixels) instead of in discrete space with pixel, and a combined classification and regression approach is adopted to perform encoding-decoding. The Unbiased Data Processing (UDP) for human pose estimation can be achieved by combining the two together. UDP not only boosts the performance of existing methods by a large margin but also plays a important role in result reproducing and future exploration. As a model-agnostic approach, UDP promotes SimpleBaseline-ResNet50-256x192 by 1.5 AP (70.2 to 71.7) and HRNet-W32-256x192 by 1.7 AP (73.5 to 75.2) on COCO test-dev set. The HRNet-W48-384x288 equipped with UDP achieves 76.5 AP and sets a new state-of-the-art for human pose estimation. The source code is publicly available for further research. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Huang_The_Devil_Is_in_the_Details_Delving_Into_Unbiased_Data_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.07524 | https://www.youtube.com/watch?v=czS2RDmHqM0 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_The_Devil_Is_in_the_Details_Delving_Into_Unbiased_Data_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_The_Devil_Is_in_the_Details_Delving_Into_Unbiased_Data_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
GraphTER: Unsupervised Learning of Graph Transformation Equivariant Representations via Auto-Encoding Node-Wise Transformations | Xiang Gao, Wei Hu, Guo-Jun Qi | Recent advances in Graph Convolutional Neural Networks (GCNNs) have shown their efficiency for nonEuclidean data on graphs, which often require a large amount of labeled data with high cost. It it thus critical to learn graph feature representations in an unsupervised manner in practice. To this end, we propose a novel unsupervised learning of Graph Transformation Equivariant Representations (GraphTER), aiming to capture intrinsic patterns of graph structure under both global and local transformations. Specifically, we allow to sample different groups of nodes from a graph and then transform them node-wise isotropically or anisotropically. Then, we self-train a representation encoder to capture the graph structures by reconstructing these node-wise transformations from the feature representations of the original and transformed graphs. In experiments, we apply the learned GraphTER to graphs of 3D point cloud data, and results on point cloud segmentation/classification show that GraphTER significantly outperforms state-of-the-art unsupervised approaches and pushes greatly closer towards the upper bound set by the fully supervised counterparts. The code is available at: https://github.com/gyshgx868/graph-ter. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gao_GraphTER_Unsupervised_Learning_of_Graph_Transformation_Equivariant_Representations_via_Auto-Encoding_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.08142 | https://www.youtube.com/watch?v=kQgRaClbdvc | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_GraphTER_Unsupervised_Learning_of_Graph_Transformation_Equivariant_Representations_via_Auto-Encoding_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_GraphTER_Unsupervised_Learning_of_Graph_Transformation_Equivariant_Representations_via_Auto-Encoding_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders | Jing Zhang, Deng-Ping Fan, Yuchao Dai, Saeed Anwar, Fatemeh Sadat Saleh, Tong Zhang, Nick Barnes | In this paper, we propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Existing RGB-D saliency detection methods treat the saliency detection task as a point estimation problem, and produce a single saliency map following a deterministic learning pipeline. Inspired by the saliency data labeling process, we propose probabilistic RGB-D saliency detection network via conditional variational autoencoders to model human annotation uncertainty and generate multiple saliency maps for each input image by sampling in the latent space. With the proposed saliency consensus process, we are able to generate an accurate saliency map based on these multiple predictions. Quantitative and qualitative evaluations on six challenging benchmark datasets against 18 competing algorithms demonstrate the effectiveness of our approach in learning the distribution of saliency maps, leading to a new state-of-the-art in RGB-D saliency detection. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_UC-Net_Uncertainty_Inspired_RGB-D_Saliency_Detection_via_Conditional_Variational_Autoencoders_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=HRaxIwDfZSk | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_UC-Net_Uncertainty_Inspired_RGB-D_Saliency_Detection_via_Conditional_Variational_Autoencoders_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_UC-Net_Uncertainty_Inspired_RGB-D_Saliency_Detection_via_Conditional_Variational_Autoencoders_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
4D Visualization of Dynamic Events From Unconstrained Multi-View Videos | Aayush Bansal, Minh Vo, Yaser Sheikh, Deva Ramanan, Srinivasa Narasimhan | We present a data-driven approach for 4D space-time visualization of dynamic events from videos captured by hand-held multiple cameras. Key to our approach is the use of self-supervised neural networks specific to the scene to compose static and dynamic aspects of an event. Though captured from discrete viewpoints, this model enables us to move around the space-time of the event continuously. This model allows us to create virtual cameras that facilitate: (1) freezing the time and exploring views; (2) freezing a view and moving through time; and (3) simultaneously changing both time and view. We can also edit the videos and reveal occluded objects for a given view if it is visible in any of the other views. We validate our approach on challenging in-the-wild events captured using up to 15 mobile cameras. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Bansal_4D_Visualization_of_Dynamic_Events_From_Unconstrained_Multi-View_Videos_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.13532 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Bansal_4D_Visualization_of_Dynamic_Events_From_Unconstrained_Multi-View_Videos_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Bansal_4D_Visualization_of_Dynamic_Events_From_Unconstrained_Multi-View_Videos_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Factorized Higher-Order CNNs With an Application to Spatio-Temporal Emotion Estimation | Jean Kossaifi, Antoine Toisoul, Adrian Bulat, Yannis Panagakis, Timothy M. Hospedales, Maja Pantic | Training deep neural networks with spatio-temporal (i.e., 3D) or multidimensional convolutions of higher-order is computationally challenging due to millions of unknown parameters across dozens of layers. To alleviate this, one approach is to apply low-rank tensor decompositions to convolution kernels in order to compress the network and reduce its number of parameters. Alternatively, new convolutional blocks, such as MobileNet, can be directly designed for efficiency. In this paper, we unify these two approaches by proposing a tensor factorization framework for efficient multidimensional (separable) convolutions of higher-order. Interestingly, the proposed framework enables a novel higher-order transduction, allowing to train a network on a given domain (e.g., 2D images or N-dimensional data in general) and using transduction to generalize to higher-order data such as videos (or (N+K)--dimensional data in general), capturing for instance temporal dynamics while preserving the learnt spatial information. We apply the proposed methodology, coined CP-Higher-Order Convolution (HO-CPConv), to spatio-temporal facial emotion analysis. Most existing facial affect models focus on static imagery and discard all temporal information. This is due to the above-mentioned burden of training 3D convolutional nets and the lack of large bodies of video data annotated by experts. We address both issues with our proposed framework. Initial training is first done on static imagery before using transduction to generalize to the temporal domain. We demonstrate superior performance on three challenging large scale affect estimation datasets, AffectNet, SEWA, and AFEW-VA. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kossaifi_Factorized_Higher-Order_CNNs_With_an_Application_to_Spatio-Temporal_Emotion_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1906.06196 | https://www.youtube.com/watch?v=TKIWcVDIQ4U | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kossaifi_Factorized_Higher-Order_CNNs_With_an_Application_to_Spatio-Temporal_Emotion_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kossaifi_Factorized_Higher-Order_CNNs_With_an_Application_to_Spatio-Temporal_Emotion_Estimation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kossaifi_Factorized_Higher-Order_CNNs_CVPR_2020_supplemental.pdf | null | null |
Hardware-in-the-Loop End-to-End Optimization of Camera Image Processing Pipelines | Ali Mosleh, Avinash Sharma, Emmanuel Onzon, Fahim Mannan, Nicolas Robidoux, Felix Heide | Commodity imaging systems rely on hardware image signal processing (ISP) pipelines. These low-level pipelines consist of a sequence of processing blocks that, depending on their hyperparameters, reconstruct a color image from RAW sensor measurements. Hardware ISP hyperparameters have a complex interaction with the output image, and therefore with the downstream application ingesting these images. Traditionally, ISPs are manually tuned in isolation by imaging experts without an end-to-end objective. Very recently, ISPs have been optimized with 1st-order methods that require differentiable approximations of the hardware ISP. Departing from such approximations, we present a hardware-in-the-loop method that directly optimizes hardware image processing pipelines for end-to-end domain-specific losses by solving a nonlinear multi-objective optimization problem with a novel 0th-order stochastic solver directly interfaced with the hardware ISP. We validate the proposed method with recent hardware ISPs and 2D object detection, segmentation, and human viewing as end-to-end downstream tasks. For automotive 2D object detection, the proposed method outperforms manual expert tuning by 30% mean average precision (mAP) and recent methods using ISP approximations by 18% mAP. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Mosleh_Hardware-in-the-Loop_End-to-End_Optimization_of_Camera_Image_Processing_Pipelines_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=IQv1wpezJPI | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Mosleh_Hardware-in-the-Loop_End-to-End_Optimization_of_Camera_Image_Processing_Pipelines_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Mosleh_Hardware-in-the-Loop_End-to-End_Optimization_of_Camera_Image_Processing_Pipelines_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Mosleh_Hardware-in-the-Loop_End-to-End_Optimization_CVPR_2020_supplemental.pdf | null | null |
VOLDOR: Visual Odometry From Log-Logistic Dense Optical Flow Residuals | Zhixiang Min, Yiding Yang, Enrique Dunn | We propose a dense indirect visual odometry method taking as input externally estimated optical flow fields instead of hand-crafted feature correspondences. We define our problem as a probabilistic model and develop a generalized-EM formulation for the joint inference of camera motion, pixel depth, and motion-track confidence. Contrary to traditional methods assuming Gaussian-distributed observation errors, we supervise our inference framework under an (empirically validated) adaptive log-logistic distribution model. Moreover, the log-logistic residual model generalizes well to different state-of-the-art optical flow methods, making our approach modular and agnostic to the choice of optical flow estimators. Our method achieved top-ranking results on both TUM RGB-D and KITTI odometry benchmarks. Our open-sourced implementation is inherently GPU-friendly with only linear computational and storage growth. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Min_VOLDOR_Visual_Odometry_From_Log-Logistic_Dense_Optical_Flow_Residuals_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Min_VOLDOR_Visual_Odometry_From_Log-Logistic_Dense_Optical_Flow_Residuals_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Min_VOLDOR_Visual_Odometry_From_Log-Logistic_Dense_Optical_Flow_Residuals_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds | Haozhe Qi, Chen Feng, Zhiguo Cao, Feng Zhao, Yang Xiao | Towards 3D object tracking in point clouds, a novel point-to-box network termed P2B is proposed in an end-to-end learning manner. Our main idea is to first localize potential target centers in 3D search area embedded with target information. Then point-driven 3D target proposal and verification are executed jointly. In this way, the time-consuming 3D exhaustive search can be avoided. Specifically, we first sample seeds from the point clouds in template and search area respectively. Then, we execute permutation-invariant feature augmentation to embed target clues from template into search area seeds and represent them with target-specific features. Consequently, the augmented search area seeds regress the potential target centers via Hough voting. The centers are further strengthened with seed-wise targetness scores. Finally, each center clusters its neighbors to leverage the ensemble power for joint 3D target proposal and verification. We apply PointNet++ as our backbone and experiments on KITTI tracking dataset demonstrate P2B's superiority ( 10%'s improvement over state-of-the-art). Note that P2B can run with 40FPS on a single NVIDIA 1080Ti GPU. Our code and model are available at https://github.com/HaozheQi/P2B. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Qi_P2B_Point-to-Box_Network_for_3D_Object_Tracking_in_Point_Clouds_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.13888 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Qi_P2B_Point-to-Box_Network_for_3D_Object_Tracking_in_Point_Clouds_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Qi_P2B_Point-to-Box_Network_for_3D_Object_Tracking_in_Point_Clouds_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Unsupervised Deep Shape Descriptor With Point Distribution Learning | Yi Shi, Mengchen Xu, Shuaihang Yuan, Yi Fang | Deep learning models have achieved great success in supervised shape descriptor learning for 3D shape retrieval, classification, and correspondence. However, the unsupervised shape descriptor calculated via deep learning is less studied than that of supervised ones due to the design challenges of unsupervised neural network architecture. This paper proposes a novel probabilistic framework for the learning of unsupervised deep shape descriptors with point distribution learning. In our approach, we firstly associate each point with a Gaussian, and the point clouds are modeled as the distribution of the points. We then use deep neural networks (DNNs) to model a maximum likelihood estimation process that is traditionally solved with an iterative Expectation-Maximization (EM) process. Our key novelty is that "training" these DNNs with unsupervised self-correspondence L2 distance loss will elegantly reveal the statically significant deep shape descriptor representation for the distribution of the point clouds. We have conducted experiments over various 3D datasets. Qualitative and quantitative comparisons demonstrate that our proposed method achieves superior classification performance over existing unsupervised 3D shape descriptors. In addition, we verified the following attractive properties of our shape descriptor through experiments: multi-scale shape representation, robustness to shape rotation, and robustness to noise. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shi_Unsupervised_Deep_Shape_Descriptor_With_Point_Distribution_Learning_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Shi_Unsupervised_Deep_Shape_Descriptor_With_Point_Distribution_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Shi_Unsupervised_Deep_Shape_Descriptor_With_Point_Distribution_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning to Transfer Texture From Clothing Images to 3D Humans | Aymen Mir, Thiemo Alldieck, Gerard Pons-Moll | In this paper, we present a simple yet effective method to automatically transfer textures of clothing images (front and back) to 3D garments worn on top SMPL, in real time. We first automatically compute training pairs of images with aligned 3D garments using a custom non-rigid 3D to 2D registration method, which is accurate but slow. Using these pairs, we learn a mapping from pixels to the 3D garment surface. Our idea is to learn dense correspondences from garment image silhouettes to a 2D-UV map of a 3D garment surface using shape information alone, completely ignoring texture, which allows us to generalize to the wide range of web images. Several experiments demonstrate that our model is more accurate than widely used baselines such as thin-plate-spline warping and image-to-image translation networks while being orders of magnitude faster. Our model opens the door for applications such as virtual try-on, and allows for generation of 3D humans with varied textures which is necessary for learning. Code will be available at https://virtualhumans.mpi-inf.mpg.de/pix2surf/. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Mir_Learning_to_Transfer_Texture_From_Clothing_Images_to_3D_Humans_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Mir_Learning_to_Transfer_Texture_From_Clothing_Images_to_3D_Humans_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Mir_Learning_to_Transfer_Texture_From_Clothing_Images_to_3D_Humans_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Mir_Learning_to_Transfer_CVPR_2020_supplemental.pdf | null | null |
Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning | Yu Deng, Jiaolong Yang, Dong Chen, Fang Wen, Xin Tong | We propose an approach for face image generation of virtual people with disentangled, precisely-controllable latent representations for identity of non-existing people, expression, pose, and illumination. We embed 3D priors into adversarial learning and train the network to imitate the image formation of an analytic 3D face deformation and rendering process. To deal with the generation freedom induced by the domain gap between real and rendered faces, we further introduce contrastive learning to promote disentanglement by comparing pairs of generated images. Experiments show that through our imitative-contrastive learning, the factor variations are very well disentangled and the properties of a generated face can be precisely controlled. We also analyze the learned latent space and present several meaningful properties supporting factor disentanglement. Our method can also be used to embed real images into the disentangled latent space. We hope our method could provide new understandings of the relationship between physical properties and deep image synthesis. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Deng_Disentangled_and_Controllable_Face_Image_Generation_via_3D_Imitative-Contrastive_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.11660 | https://www.youtube.com/watch?v=crwYR2E5FU4 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Deng_Disentangled_and_Controllable_Face_Image_Generation_via_3D_Imitative-Contrastive_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Deng_Disentangled_and_Controllable_Face_Image_Generation_via_3D_Imitative-Contrastive_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Multi-Scale Interactive Network for Salient Object Detection | Youwei Pang, Xiaoqi Zhao, Lihe Zhang, Huchuan Lu | Deep-learning based salient object detection methods achieve great progress. However, the variable scale and unknown category of salient objects are great challenges all the time. These are closely related to the utilization of multi-level and multi-scale features. In this paper, we propose the aggregate interaction modules to integrate the features from adjacent levels, in which less noise is introduced because of only using small up-/down-sampling rates. To obtain more efficient multi-scale features from the integrated features, the self-interaction modules are embedded in each decoder unit. Besides, the class imbalance issue caused by the scale variation weakens the effect of the binary cross entropy loss and results in the spatial inconsistency of the predictions. Therefore, we exploit the consistency-enhanced loss to highlight the fore-/back-ground difference and preserve the intra-class consistency. Experimental results on five benchmark datasets demonstrate that the proposed method without any post-processing performs favorably against 23 state-of-the-art approaches. The source code will be publicly available at https://github.com/lartpang/MINet. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Pang_Multi-Scale_Interactive_Network_for_Salient_Object_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/2007.09062 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Pang_Multi-Scale_Interactive_Network_for_Salient_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Pang_Multi-Scale_Interactive_Network_for_Salient_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Correlation-Guided Attention for Corner Detection Based Visual Tracking | Fei Du, Peng Liu, Wei Zhao, Xianglong Tang | Accurate bounding box estimation has recently attracted much attention in the tracking community because traditional multi-scale search strategies cannot estimate tight bounding boxes in many challenging scenarios involving changes to the target. A tracker capable of detecting target corners can flexibly adapt to such changes, but existing corner detection based tracking methods have not achieved adequate success. We analyze the reasons for their failure and propose a state-of-the-art tracker that performs correlation-guided attentional corner detection in two stages. First, a region of interest (RoI) is obtained by employing an efficient Siamese network to distinguish the target from the background. Second, a pixel-wise correlation-guided spatial attention module and a channel-wise correlation-guided channel attention module exploit the relationship between the target template and the RoI to highlight corner regions and enhance features of the RoI for corner detection. The correlation-guided attention modules improve the accuracy of corner detection, thus enabling accurate bounding box estimation. When trained on large-scale datasets using a novel RoI augmentation strategy, the performance of the proposed tracker, running at a high speed of 70 FPS, is comparable with that of state-of-the-art trackers in meeting five challenging performance benchmarks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Du_Correlation-Guided_Attention_for_Corner_Detection_Based_Visual_Tracking_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Du_Correlation-Guided_Attention_for_Corner_Detection_Based_Visual_Tracking_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Du_Correlation-Guided_Attention_for_Corner_Detection_Based_Visual_Tracking_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Du_Correlation-Guided_Attention_for_CVPR_2020_supplemental.pdf | null | null |
Accurate Estimation of Body Height From a Single Depth Image via a Four-Stage Developing Network | Fukun Yin, Shizhe Zhou | Non-contact measurement of human body height can be very difficult under some circumstances.In this paper we address the problem of accurately estimating the height of a person with arbitrary postures from a single depth image. By introducing a novel part-based intermediate representation plus a four-stage increasingly complex deep neural network, we manage to achieve significantly higher accuracy than previous methods. We first describe the human body in the form of a segmentation of human torso as four nearly rigid parts and then predict their lengths respectively by 3 CNNs. Instead of directly adding the lengths of these parts together, we further construct another independent developing CNN that combines the intermediate representation, part lengths and depth information together to finally predict the body height results.Here we develop an increasingly complex network architecture and adopt a hybrid pooling to optimize training process. To the best of our knowledge, this is the first method that estimates height only from a single depth image. In experiments our average accuracy reaches at 99.1% for people in various positions and postures. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yin_Accurate_Estimation_of_Body_Height_From_a_Single_Depth_Image_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=f66KDYdqEKI | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yin_Accurate_Estimation_of_Body_Height_From_a_Single_Depth_Image_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yin_Accurate_Estimation_of_Body_Height_From_a_Single_Depth_Image_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-Identification | Yunpeng Zhai, Shijian Lu, Qixiang Ye, Xuebo Shan, Jie Chen, Rongrong Ji, Yonghong Tian | Domain adaptive person re-identification (re-ID) is a challenging task, especially when person identities in target domains are unknown. Existing methods attempt to address this challenge by transferring image styles or aligning feature distributions across domains, whereas the rich unlabeled samples in target domains are not sufficiently exploited. This paper presents a novel augmented discriminative clustering (AD-Cluster) technique that estimates and augments person clusters in target domains and enforces the discrimination ability of re-ID models with the augmented clusters. AD-Cluster is trained by iterative density-based clustering, adaptive sample augmentation, and discriminative feature learning. It learns an image generator and a feature encoder which aim to maximize the intra-cluster diversity in the sample space and minimize the intra-cluster distance in the feature space in an adversarial min-max manner. Finally, AD-Cluster increases the diversity of sample clusters and improves the discrimination capability of re-ID models greatly. Extensive experiments over Market-1501 and DukeMTMC-reID show that AD-Cluster outperforms the state-of-the-art with large margins. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhai_AD-Cluster_Augmented_Discriminative_Clustering_for_Domain_Adaptive_Person_Re-Identification_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=QQhu3IJk7jQ | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhai_AD-Cluster_Augmented_Discriminative_Clustering_for_Domain_Adaptive_Person_Re-Identification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhai_AD-Cluster_Augmented_Discriminative_Clustering_for_Domain_Adaptive_Person_Re-Identification_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Regularizing Neural Networks via Minimizing Hyperspherical Energy | Rongmei Lin, Weiyang Liu, Zhen Liu, Chen Feng, Zhiding Yu, James M. Rehg, Li Xiong, Le Song | Inspired by the Thomson problem in physics where the distribution of multiple propelling electrons on a unit sphere can be modeled via minimizing some potential energy, hyperspherical energy minimization has demonstrated its potential in regularizing neural networks and improving their generalization power. In this paper, we first study the important role that hyperspherical energy plays in neural network training by analyzing its training dynamics. Then we show that naively minimizing hyperspherical energy suffers from some difficulties due to highly non-linear and non-convex optimization as the space dimensionality becomes higher, therefore limiting the potential to further improve the generalization. To address these problems, we propose the compressive minimum hyperspherical energy (CoMHE) as a more effective regularization for neural networks. Specifically, CoMHE utilizes projection mappings to reduce the dimensionality of neurons and minimizes their hyperspherical energy. According to different designs for the projection mapping, we propose several distinct yet well-performing variants and provide some theoretical guarantees to justify their effectiveness. Our experiments show that CoMHE consistently outperforms existing regularization methods, and can be easily applied to different neural networks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lin_Regularizing_Neural_Networks_via_Minimizing_Hyperspherical_Energy_CVPR_2020_paper.pdf | http://arxiv.org/abs/1906.04892 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Regularizing_Neural_Networks_via_Minimizing_Hyperspherical_Energy_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Regularizing_Neural_Networks_via_Minimizing_Hyperspherical_Energy_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lin_Regularizing_Neural_Networks_CVPR_2020_supplemental.pdf | null | null |
Density-Aware Feature Embedding for Face Clustering | Senhui Guo, Jing Xu, Dapeng Chen, Chao Zhang, Xiaogang Wang, Rui Zhao | Clustering has many applications in research and industry. However, traditional clustering methods, such as K-means, DBSCAN and HAC, impose oversimplifying assumptions and thus are not well-suited to face clustering. To adapt to the distribution of realistic problems, a natural approach is to use Graph Convolutional Networks (GCNs) to enhance features for clustering. However, GCNs can only utilize local information, which ignores the overall characterisitcs of the clusters. In this paper, we propose a Density-Aware Feature Embedding Network (DA-Net) for the task of face clustering, which utilizes both local and non-local information, to learn a robust feature embedding. Specifically, DA-Net uses GCNs to aggregate features locally, and then incorporates non-local information using a density chain, which is a chain of faces from low density to high density. This density chain exploits the non-uniform distribution of face images in the dataset. Then, an LSTM takes the density chain as input to generate the final feature embedding. Once this embedding is generated, traditional clustering methods, such as density-based clustering, can be used to obtain the final clustering results. Extensive experiments verify the effectiveness of the proposed feature embedding method, which can achieve state-of-the-art performance on public benchmarks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Guo_Density-Aware_Feature_Embedding_for_Face_Clustering_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Density-Aware_Feature_Embedding_for_Face_Clustering_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Density-Aware_Feature_Embedding_for_Face_Clustering_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning to Manipulate Individual Objects in an Image | Yanchao Yang, Yutong Chen, Stefano Soatto | We describe a method to train a generative model with latent factors that are (approximately) independent and localized. This means that perturbing the latent variables affects only local regions of the synthesized image, corresponding to objects. Unlike other unsupervised generative models, ours enables object-centric manipulation, without requiring object-level annotations, or any form of annotation for that matter. The key to our method is the combination of spatial disentanglement, enforced by a Contextual Information Separation loss, and perceptual cycle-consistency, enforced by a loss that penalizes changes in the image partition in response to perturbations of the latent factors. We test our method's ability to allow independent control of spatial and semantic factors of variability on existing datasets and also introduce two new ones that highlight the limitations of current methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_Learning_to_Manipulate_Individual_Objects_in_an_Image_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.05495 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Learning_to_Manipulate_Individual_Objects_in_an_Image_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Learning_to_Manipulate_Individual_Objects_in_an_Image_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
3D Photography Using Context-Aware Layered Depth Inpainting | Meng-Li Shih, Shih-Yang Su, Johannes Kopf, Jia-Bin Huang | We propose a method for converting a single RGB-D input image into a 3D photo, i.e., a multi-layer representation for novel view synthesis that contains hallucinated color and depth structures in regions occluded in the original view. We use a Layered Depth Image with explicit pixel connectivity as underlying representation, and present a learning-based inpainting model that iteratively synthesizes new local color-and-depth content into the occluded region in a spatial context-aware manner. The resulting 3D photos can be efficiently rendered with motion parallax using standard graphics engines. We validate the effectiveness of our method on a wide range of challenging everyday scenes and show less artifacts when compared with the state-of-the-arts. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shih_3D_Photography_Using_Context-Aware_Layered_Depth_Inpainting_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.04727 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Shih_3D_Photography_Using_Context-Aware_Layered_Depth_Inpainting_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Shih_3D_Photography_Using_Context-Aware_Layered_Depth_Inpainting_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Grid-GCN for Fast and Scalable Point Cloud Learning | Qiangeng Xu, Xudong Sun, Cho-Ying Wu, Panqu Wang, Ulrich Neumann | Due to the sparsity and irregularity of the point cloud data, methods that directly consume points have become popular. Among all point-based models, graph convolutional networks (GCN) lead to notable performance by fully preserving the data granularity and exploiting point interrelation. However, point-based networks spend a significant amount of time on data structuring (e.g., Farthest Point Sampling (FPS) and neighbor points querying), which limit the speed and scalability. In this paper, we present a method, named Grid-GCN, for fast and scalable point cloud learning. Grid-GCN uses a novel data structuring strategy, Coverage-Aware Grid Query (CAGQ). By leveraging the efficiency of grid space, CAGQ improves spatial coverage while reducing the theoretical time complexity. Compared with popular sampling methods such as Farthest Point Sampling (FPS) and Ball Query, CAGQ achieves up to 50 times speed-up. With a Grid Context Aggregation (GCA) module, Grid-GCN achieves state-of-the-art performance on major point cloud classification and segmentation benchmarks with significantly faster runtime than previous studies. Remarkably, Grid-GCN achieves the inference speed of 50FPS on ScanNet using 81920 points as input. The supplementary xharlie.github.io/papers/GGCN_supCamReady.pdf and the code github.com/xharlie/Grid-GCN are released. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xu_Grid-GCN_for_Fast_and_Scalable_Point_Cloud_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.02984 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Grid-GCN_for_Fast_and_Scalable_Point_Cloud_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Grid-GCN_for_Fast_and_Scalable_Point_Cloud_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
KFNet: Learning Temporal Camera Relocalization Using Kalman Filtering | Lei Zhou, Zixin Luo, Tianwei Shen, Jiahui Zhang, Mingmin Zhen, Yao Yao, Tian Fang, Long Quan | Temporal camera relocalization estimates the pose with respect to each video frame in sequence, as opposed to one-shot relocalization which focuses on a still image. Even though the time dependency has been taken into account, current temporal relocalization methods still generally underperform the state-of-the-art one-shot approaches in terms of accuracy. In this work, we improve the temporal relocalization method by using a network architecture that incorporates Kalman filtering (KFNet) for online camera relocalization. In particular, KFNet extends the scene coordinate regression problem to the time domain in order to recursively establish 2D and 3D correspondences for the pose determination. The network architecture design and the loss formulation are based on Kalman filtering in the context of Bayesian learning. Extensive experiments on multiple relocalization benchmarks demonstrate the high accuracy of KFNet at the top of both one-shot and temporal relocalization approaches. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhou_KFNet_Learning_Temporal_Camera_Relocalization_Using_Kalman_Filtering_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.10629 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_KFNet_Learning_Temporal_Camera_Relocalization_Using_Kalman_Filtering_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_KFNet_Learning_Temporal_Camera_Relocalization_Using_Kalman_Filtering_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhou_KFNet_Learning_Temporal_CVPR_2020_supplemental.pdf | null | null |
SuperGlue: Learning Feature Matching With Graph Neural Networks | Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich | This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points. Assignments are estimated by solving a differentiable optimal transport problem, whose costs are predicted by a graph neural network. We introduce a flexible context aggregation mechanism based on attention, enabling SuperGlue to reason about the underlying 3D scene and feature assignments jointly. Compared to traditional, hand-designed heuristics, our technique learns priors over geometric transformations and regularities of the 3D world through end-to-end training from image pairs. SuperGlue outperforms other learned approaches and achieves state-of-the-art results on the task of pose estimation in challenging real-world indoor and outdoor environments. The proposed method performs matching in real-time on a modern GPU and can be readily integrated into modern SfM or SLAM systems. The code and trained weights are publicly available at github.com/magicleap/SuperGluePretrainedNetwork. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Sarlin_SuperGlue_Learning_Feature_Matching_With_Graph_Neural_Networks_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.11763 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Sarlin_SuperGlue_Learning_Feature_Matching_With_Graph_Neural_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Sarlin_SuperGlue_Learning_Feature_Matching_With_Graph_Neural_Networks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Sarlin_SuperGlue_Learning_Feature_CVPR_2020_supplemental.pdf | null | null |
Probabilistic Structural Latent Representation for Unsupervised Embedding | Mang Ye, Jianbing Shen | Unsupervised embedding learning aims at extracting low-dimensional visually meaningful representations from large-scale unlabeled images, which can then be directly used for similarity-based search. This task faces two major challenges: 1) mining positive supervision from highly similar fine-grained classes and 2) generating to unseen testing categories. To tackle these issues, this paper proposes a probabilistic structural latent representation (PSLR), which incorporates an adaptable softmax embedding to approximate the positive concentrated and negative instance separated properties in the graph latent space. It improves the discriminability by enlarging the positive/negative difference without introducing any additional computational cost while maintaining high learning efficiency. To address the limited supervision using data augmentation, a smooth variational reconstruction loss is introduced by modeling the intra-instance variance, which improves the robustness. Extensive experiments demonstrate the superiority of PSLR over state-of-the-art unsupervised methods on both seen and unseen categories with cosine similarity. Code is available at https://github.com/mangye16/PSLR | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ye_Probabilistic_Structural_Latent_Representation_for_Unsupervised_Embedding_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ye_Probabilistic_Structural_Latent_Representation_for_Unsupervised_Embedding_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ye_Probabilistic_Structural_Latent_Representation_for_Unsupervised_Embedding_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
How Useful Is Self-Supervised Pretraining for Visual Tasks? | Alejandro Newell, Jia Deng | Recent advances have spurred incredible progress in self-supervised pretraining for vision. We investigate what factors may play a role in the utility of these pretraining methods for practitioners. To do this, we evaluate various self-supervised algorithms across a comprehensive array of synthetic datasets and downstream tasks. We prepare a suite of synthetic data that enables an endless supply of annotated images as well as full control over dataset difficulty. Our experiments offer insights into how the utility of self-supervision changes as the number of available labels grows as well as how the utility changes as a function of the downstream task and the properties of the training data. We also find that linear evaluation does not correlate with finetuning performance. Code and data is available at \href https://www.github.com/princeton-vl/selfstudy github.com/princeton-vl/selfstudy . | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Newell_How_Useful_Is_Self-Supervised_Pretraining_for_Visual_Tasks_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.14323 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Newell_How_Useful_Is_Self-Supervised_Pretraining_for_Visual_Tasks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Newell_How_Useful_Is_Self-Supervised_Pretraining_for_Visual_Tasks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Newell_How_Useful_Is_CVPR_2020_supplemental.pdf | null | null |
Action Segmentation With Joint Self-Supervised Temporal Domain Adaptation | Min-Hung Chen, Baopu Li, Yingze Bao, Ghassan AlRegib, Zsolt Kira | Despite the recent progress of fully-supervised action segmentation techniques, the performance is still not fully satisfactory. One main challenge is the problem of spatiotemporal variations (e.g. different people may perform the same activity in various ways). Therefore, we exploit unlabeled videos to address this problem by reformulating the action segmentation task as a cross-domain problem with domain discrepancy caused by spatio-temporal variations. To reduce the discrepancy, we propose SelfSupervised Temporal Domain Adaptation (SSTDA), which contains two self-supervised auxiliary tasks (binary and sequential domain prediction) to jointly align cross-domain feature spaces embedded with local and global temporal dynamics, achieving better performance than other Domain Adaptation (DA) approaches. On three challenging benchmark datasets (GTEA, 50Salads, and Breakfast), SSTDA outperforms the current state-of-the-art method by large margins (e.g. for the F1@25 score, from 59.6% to 69.1% on Breakfast, from 73.4% to 81.5% on 50Salads, and from 83.6% to 89.1% on GTEA), and requires only 65% of the labeled training data for comparable performance, demonstrating the usefulness of adapting to unlabeled target videos across variations. The source code is available at https://github.com/cmhungsteve/SSTDA. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Action_Segmentation_With_Joint_Self-Supervised_Temporal_Domain_Adaptation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.02824 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Action_Segmentation_With_Joint_Self-Supervised_Temporal_Domain_Adaptation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Action_Segmentation_With_Joint_Self-Supervised_Temporal_Domain_Adaptation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_Action_Segmentation_With_CVPR_2020_supplemental.pdf | null | null |
Regularizing Discriminative Capability of CGANs for Semi-Supervised Generative Learning | Yi Liu, Guangchang Deng, Xiangping Zeng, Si Wu, Zhiwen Yu, Hau-San Wong | Semi-supervised generative learning aims to learn the underlying class-conditional distribution of partially labeled data. Generative Adversarial Networks (GANs) have led to promising progress in this task. However, it still needs to further explore the issue of imbalance between real labeled data and fake data in the adversarial learning process. To address this issue, we propose a regularization technique based on Random Regional Replacement (R^3-regularization) to facilitate the generative learning process. Specifically, we construct two types of between-class instances: cross-category ones and real-fake ones. These instances could be closer to the decision boundaries and are important for regularizing the classification and discriminative networks in our class-conditional GANs, which we refer to as R^3-CGAN. Better guidance from these two networks makes the generative network produce instances with class-specific information and high fidelity. We experiment with multiple standard benchmarks, and demonstrate that the R^3-regularization can lead to significant improvement in both classification and class-conditional image synthesis. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Regularizing_Discriminative_Capability_of_CGANs_for_Semi-Supervised_Generative_Learning_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=kWfCXjXUQaE | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Regularizing_Discriminative_Capability_of_CGANs_for_Semi-Supervised_Generative_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Regularizing_Discriminative_Capability_of_CGANs_for_Semi-Supervised_Generative_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
State-Relabeling Adversarial Active Learning | Beichen Zhang, Liang Li, Shijie Yang, Shuhui Wang, Zheng-Jun Zha, Qingming Huang | Active learning is to design label-efficient algorithms by sampling the most representative samples to be labeled by an oracle. In this paper, we propose a state relabeling adversarial active learning model (SRAAL), that leverages both the annotation and the labeled/unlabeled state information for deriving the most informative unlabeled samples. The SRAAL consists of a representation generator and a state discriminator. The generator uses the complementary annotation information with traditional reconstruction information to generate the unified representation of samples, which embeds the semantic into the whole data representation. Then, we design an online uncertainty indicator in the discriminator, which endues unlabeled samples with different importance. As a result, we can select the most informative samples based on the discriminator's predicted state. We also design an algorithm to initialize the labeled pool, which makes subsequent sampling more efficient. The experiments conducted on various datasets show that our model outperforms the previous state-of-art active learning methods and our initially sampling algorithm achieves better performance. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_State-Relabeling_Adversarial_Active_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.04943 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_State-Relabeling_Adversarial_Active_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_State-Relabeling_Adversarial_Active_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression | Yawei Li, Shuhang Gu, Christoph Mayer, Luc Van Gool, Radu Timofte | In this paper, we analyze two popular network compression techniques, i.e. filter pruning and low-rank decomposition, in a unified sense. By simply changing the way the sparsity regularization is enforced, filter pruning and low-rank decomposition can be derived accordingly. This provides another flexible choice for network compression because the techniques complement each other. For example, in popular network architectures with shortcut connections (e.g. ResNet), filter pruning cannot deal with the last convolutional layer in a ResBlock while the low-rank decomposition methods can. In addition, we propose to compress the whole network jointly instead of in a layer-wise manner. Our approach proves its potential as it compares favorably to the state-of-the-art on several benchmarks. Code is available at https://github.com/ofsoundof/group_sparsity. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Group_Sparsity_The_Hinge_Between_Filter_Pruning_and_Decomposition_for_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.08935 | https://www.youtube.com/watch?v=pDB2HPOziVU | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Group_Sparsity_The_Hinge_Between_Filter_Pruning_and_Decomposition_for_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Group_Sparsity_The_Hinge_Between_Filter_Pruning_and_Decomposition_for_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Group_Sparsity_The_CVPR_2020_supplemental.pdf | null | null |
P-nets: Deep Polynomial Neural Networks | Grigorios G. Chrysos, Stylianos Moschoglou, Giorgos Bouritsas, Yannis Panagakis, Jiankang Deng, Stefanos Zafeiriou | Deep Convolutional Neural Networks (DCNNs) is currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. The success of DCNNs can be attributed to the careful selection of their building blocks (e.g., residual blocks, rectifiers, sophisticated normalization schemes, to mention but a few). In this paper, we propose \Pi-Nets, a new class of DCNNs. \Pi-Nets are polynomial neural networks, i.e., the output is a high-order polynomial of the input. \Pi-Nets can be implemented using special kind of skip connections and their parameters can be represented via high-order tensors. We empirically demonstrate that \Pi-Nets have better representation power than standard DCNNs and they even produce good results without the use of non-linear activation functions in a large battery of tasks and signals, i.e., images, graphs, and audio. When used in conjunction with activation functions, \Pi-Nets produce state-of-the-art results in challenging tasks, such as image generation. Lastly, our framework elucidates why recent generative models, such as StyleGAN, improve upon their predecessors, e.g., ProGAN. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chrysos_P-nets_Deep_Polynomial_Neural_Networks_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chrysos_P-nets_Deep_Polynomial_Neural_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chrysos_P-nets_Deep_Polynomial_Neural_Networks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chrysos_P-nets_Deep_Polynomial_CVPR_2020_supplemental.pdf | null | null |
G3AN: Disentangling Appearance and Motion for Video Generation | Yaohui Wang, Piotr Bilinski, Francois Bremond, Antitza Dantcheva | Creating realistic human videos entails the challenge of being able to simultaneously generate both appearance, as well as motion. To tackle this challenge, we introduce G3AN, a novel spatio-temporal generative model, which seeks to capture the distribution of high dimensional video data and to model appearance and motion in disentangled manner. The latter is achieved by decomposing appearance and motion in a three-stream Generator, where the main stream aims to model spatio-temporal consistency, whereas the two auxiliary streams augment the main stream with multi-scale appearance and motion features, respectively. An extensive quantitative and qualitative analysis shows that our model systematically and significantly outperforms state-of-the-art methods on the facial expression datasets MUG and UvA-NEMO, as well as the Weizmann and UCF101 datasets on human action. Additional analysis on the learned latent representations confirms the successful decomposition of appearance and motion. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_G3AN_Disentangling_Appearance_and_Motion_for_Video_Generation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.05523 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_G3AN_Disentangling_Appearance_and_Motion_for_Video_Generation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_G3AN_Disentangling_Appearance_and_Motion_for_Video_Generation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_G3AN_Disentangling_Appearance_CVPR_2020_supplemental.pdf | null | null |
DeepFaceFlow: In-the-Wild Dense 3D Facial Motion Estimation | Mohammad Rami Koujan, Anastasios Roussos, Stefanos Zafeiriou | Dense 3D facial motion capture from only monocular in-the-wild pairs of RGB images is a highly challenging problem with numerous applications, ranging from facial expression recognition to facial reenactment. In this work, we propose DeepFaceFlow, a robust, fast, and highly-accurate framework for the dense estimation of 3D non-rigid facial flow between pairs of monocular images. Our DeepFaceFlow framework was trained and tested on two very large-scale facial video datasets, one of them of our own collection and annotation, with the aid of occlusion-aware and 3D-based loss function. We conduct comprehensive experiments probing different aspects of our approach and demonstrating its improved performance against state-of-the-art flow and 3D reconstruction methods. Furthermore, we incorporate our framework in a full-head state-of-the-art facial video synthesis method and demonstrate the ability of our method in better representing and capturing the facial dynamics, resulting in a highly-realistic facial video synthesis. Given registered pairs of images, our framework generates 3D flow maps at 60 fps. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Koujan_DeepFaceFlow_In-the-Wild_Dense_3D_Facial_Motion_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.07298 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Koujan_DeepFaceFlow_In-the-Wild_Dense_3D_Facial_Motion_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Koujan_DeepFaceFlow_In-the-Wild_Dense_3D_Facial_Motion_Estimation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Koujan_DeepFaceFlow_In-the-Wild_Dense_CVPR_2020_supplemental.pdf | null | null |
TubeTK: Adopting Tubes to Track Multi-Object in a One-Step Training Model | Bo Pang, Yizhuo Li, Yifan Zhang, Muchen Li, Cewu Lu | Multi-object tracking is a fundamental vision problem that has been studied for a long time. As deep learning brings excellent performances to object detection algorithms, Tracking by Detection (TBD) has become the mainstream tracking framework. Despite the success of TBD, this two-step method is too complicated to train in an end-to-end manner and induces many challenges as well, such as insufficient exploration of video spatial-temporal information, vulnerability when facing object occlusion, and excessive reliance on detection results. To address these challenges, we propose a concise end-to-end model TubeTK which only needs one step training by introducing the "bounding-tube" to indicate temporal-spatial locations of objects in a short video clip. TubeTK provides a novel direction of multi-object tracking, and we demonstrate its potential to solve the above challenges without bells and whistles. We analyze the performance of TubeTK on several MOT benchmarks and provide empirical evidence to show that TubeTK has the ability to overcome occlusions to some extent without any ancillary technologies like Re-ID. Compared with other methods that adopt private detection results, our one-stage end-to-end model achieves state-of-the-art performances even if it adopts no ready-made detection results. We hope that the proposed TubeTK model can serve as a simple but strong alternative for video-based MOT task. The code and model will be publicly available accompanying this paper. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Pang_TubeTK_Adopting_Tubes_to_Track_Multi-Object_in_a_One-Step_Training_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.05683 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Pang_TubeTK_Adopting_Tubes_to_Track_Multi-Object_in_a_One-Step_Training_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Pang_TubeTK_Adopting_Tubes_to_Track_Multi-Object_in_a_One-Step_Training_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Pang_TubeTK_Adopting_Tubes_CVPR_2020_supplemental.pdf | null | null |
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