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Vehicle Trajectory Prediction Works, but Not Everywhere | Mohammadhossein Bahari, Saeed Saadatnejad, Ahmad Rahimi, Mohammad Shaverdikondori, Amir Hossein Shahidzadeh, Seyed-Mohsen Moosavi-Dezfooli, Alexandre Alahi | Vehicle trajectory prediction is nowadays a fundamental pillar of self-driving cars. Both the industry and research communities have acknowledged the need for such a pillar by providing public benchmarks. While state-of-the-art methods are impressive, i.e., they have no off-road prediction, their generalization to cities outside of the benchmark remains unexplored. In this work, we show that those methods do not generalize to new scenes. We present a novel method that automatically generates realistic scenes causing state-of-the-art models to go off-road. We frame the problem through the lens of adversarial scene generation. The method is a simple yet effective generative model based on atomic scene generation functions along with physical constraints. Our experiments show that more than 60% of existing scenes from the current benchmarks can be modified in a way to make prediction methods fail (i.e., predicting off-road). We further show that the generated scenes (i) are realistic since they do exist in the real world, and (ii) can be used to make existing models more robust, yielding 30-40% reductions in the off-road rate. The code is available online: https://s-attack.github.io/ | https://openaccess.thecvf.com/content/CVPR2022/papers/Bahari_Vehicle_Trajectory_Prediction_Works_but_Not_Everywhere_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bahari_Vehicle_Trajectory_Prediction_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.03909 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bahari_Vehicle_Trajectory_Prediction_Works_but_Not_Everywhere_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bahari_Vehicle_Trajectory_Prediction_Works_but_Not_Everywhere_CVPR_2022_paper.html | CVPR 2022 | null |
PSMNet: Position-Aware Stereo Merging Network for Room Layout Estimation | Haiyan Wang, Will Hutchcroft, Yuguang Li, Zhiqiang Wan, Ivaylo Boyadzhiev, Yingli Tian, Sing Bing Kang | In this paper, we propose a new deep learning-based method for estimating room layout given a pair of 360 panoramas. Our system, called Position-aware Stereo Merging Network or PSMNet, is an end-to-end joint layout-pose estimator. PSMNet consists of a Stereo Pano Pose (SP^2) transformer and a novel Cross-Perspective Projection (CP^2) layer. The stereo-view SP^2 transformer is used to implicitly infer correspondences between views, and can handle noisy poses. The pose-aware CP^2layer is designed to render features from the adjacent view to the anchor (reference) view, in order to perform view fusion and estimate the visible layout. Our experiments and analysis validate our method, which significantly outperforms the state-of-the-art layout estimators, especially for large and complex room spaces. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_PSMNet_Position-Aware_Stereo_Merging_Network_for_Room_Layout_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_PSMNet_Position-Aware_Stereo_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15965 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_PSMNet_Position-Aware_Stereo_Merging_Network_for_Room_Layout_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_PSMNet_Position-Aware_Stereo_Merging_Network_for_Room_Layout_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
MonoDTR: Monocular 3D Object Detection With Depth-Aware Transformer | Kuan-Chih Huang, Tsung-Han Wu, Hung-Ting Su, Winston H. Hsu | Monocular 3D object detection is an important yet challenging task in autonomous driving. Some existing methods leverage depth information from an off-the-shelf depth estimator to assist 3D detection, but suffer from the additional computational burden and achieve limited performance caused by inaccurate depth priors. To alleviate this, we propose MonoDTR, a novel end-to-end depth-aware transformer network for monocular 3D object detection. It mainly consists of two components: (1) the Depth-Aware Feature Enhancement (DFE) module that implicitly learns depth-aware features with auxiliary supervision without requiring extra computation, and (2) the Depth-Aware Transformer (DTR) module that globally integrates context- and depth-aware features. Moreover, different from conventional pixel-wise positional encodings, we introduce a novel depth positional encoding (DPE) to inject depth positional hints into transformers. Our proposed depth-aware modules can be easily plugged into existing image-only monocular 3D object detectors to improve the performance. Extensive experiments on the KITTI dataset demonstrate that our approach outperforms previous state-of-the-art monocular-based methods and achieves real-time detection. Code is available at https://github.com/kuanchihhuang/MonoDTR. | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_MonoDTR_Monocular_3D_Object_Detection_With_Depth-Aware_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_MonoDTR_Monocular_3D_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.10981 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_MonoDTR_Monocular_3D_Object_Detection_With_Depth-Aware_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_MonoDTR_Monocular_3D_Object_Detection_With_Depth-Aware_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Graph Regularisation for Guided Super-Resolution | Riccardo de Lutio, Alexander Becker, Stefano D'Aronco, Stefania Russo, Jan D. Wegner, Konrad Schindler | We introduce a novel formulation for guided super-resolution. Its core is a differentiable optimisation layer that operates on a learned affinity graph. The learned graph potentials make it possible to leverage rich contextual information from the guide image, while the explicit graph optimisation within the architecture guarantees rigorous fidelity of the high-resolution target to the low-resolution source. With the decision to employ the source as a constraint rather than only as an input to the prediction, our method differs from state-of-the-art deep architectures for guided super-resolution, which produce targets that, when downsampled, will only approximately reproduce the source. This is not only theoretically appealing, but also produces crisper, more natural-looking images. A key property of our method is that, although the graph connectivity is restricted to the pixel lattice, the associated edge potentials are learned with a deep feature extractor and can encode rich context information over large receptive fields. By taking advantage of the sparse graph connectivity, it becomes possible to propagate gradients through the optimisation layer and learn the edge potentials from data. We extensively evaluate our method on several datasets, and consistently outperform recent baselines in terms of quantitative reconstruction errors, while also delivering visually sharper outputs. Moreover, we demonstrate that our method generalises particularly well to new datasets not seen during training. | https://openaccess.thecvf.com/content/CVPR2022/papers/de_Lutio_Learning_Graph_Regularisation_for_Guided_Super-Resolution_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/de_Lutio_Learning_Graph_Regularisation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14297 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/de_Lutio_Learning_Graph_Regularisation_for_Guided_Super-Resolution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/de_Lutio_Learning_Graph_Regularisation_for_Guided_Super-Resolution_CVPR_2022_paper.html | CVPR 2022 | null |
Instance-Wise Occlusion and Depth Orders in Natural Scenes | Hyunmin Lee, Jaesik Park | In this paper, we introduce a new dataset, named InstaOrder, that can be used to understand the spatial relationships of instances in a 3D space. The dataset consists of 2.9M annotations of geometric orderings for class-labeled instances in 101K natural scenes. The scenes were annotated by 3,659 crowd-workers regarding (1) occlusion order that identifies occluder/occludee and (2) depth order that describes ordinal relations that consider relative distance from the camera. The dataset provides joint annotation of two kinds of orderings for the same instances, and we discover that the occlusion order and depth order are complementary. We also introduce a geometric order prediction network called InstaOrderNet, which is superior to state-of-the-art approaches. Moreover, we propose a dense depth prediction network called InstaDepthNet that uses auxiliary geometric order loss to boost the accuracy of the state-of-the-art depth prediction approach, MiDaS [54]. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_Instance-Wise_Occlusion_and_Depth_Orders_in_Natural_Scenes_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lee_Instance-Wise_Occlusion_and_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.14562 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Instance-Wise_Occlusion_and_Depth_Orders_in_Natural_Scenes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Instance-Wise_Occlusion_and_Depth_Orders_in_Natural_Scenes_CVPR_2022_paper.html | CVPR 2022 | null |
Look for the Change: Learning Object States and State-Modifying Actions From Untrimmed Web Videos | Tomáš Souček, Jean-Baptiste Alayrac, Antoine Miech, Ivan Laptev, Josef Sivic | Human actions often induce changes of object states such as "cutting an apple", "cleaning shoes" or "pouring coffee". In this paper, we seek to temporally localize object states (e.g. "empty" and "full" cup) together with the corresponding state-modifying actions ("pouring coffee") in long uncurated videos with minimal supervision. The contributions of this work are threefold. First, we develop a self-supervised model for jointly learning state-modifying actions together with the corresponding object states from an uncurated set of videos from the Internet. The model is self-supervised by the causal ordering signal, i.e. initial object state -> manipulating action -> end state. Second, to cope with noisy uncurated training data, our model incorporates a noise adaptive weighting module supervised by a small number of annotated still images, that allows to efficiently filter out irrelevant videos during training. Third, we collect a new dataset with more than 2600 hours of video and 34 thousand changes of object states, and manually annotate a part of this data to validate our approach. Our results demonstrate substantial improvements over prior work in both action and object state-recognition in video. | https://openaccess.thecvf.com/content/CVPR2022/papers/Soucek_Look_for_the_Change_Learning_Object_States_and_State-Modifying_Actions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Soucek_Look_for_the_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Soucek_Look_for_the_Change_Learning_Object_States_and_State-Modifying_Actions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Soucek_Look_for_the_Change_Learning_Object_States_and_State-Modifying_Actions_CVPR_2022_paper.html | CVPR 2022 | null |
Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-Shot Learning | Yangji He, Weihan Liang, Dongyang Zhao, Hong-Yu Zhou, Weifeng Ge, Yizhou Yu, Wenqiang Zhang | This paper presents new hierarchically cascaded transformers that can improve data efficiency through attribute surrogates learning and spectral tokens pooling. Vision transformers have recently been thought of as a promising alternative to convolutional neural networks for visual recognition. But when there is no sufficient data, it gets stuck in overfitting and shows inferior performance. To improve data efficiency, we propose hierarchically cascaded transformers that exploit intrinsic image structures through spectral tokens pooling and optimize the learnable parameters through latent attribute surrogates. The intrinsic image structure is utilized to reduce the ambiguity between foreground content and background noise by spectral tokens pooling. And the attribute surrogate learning scheme is designed to benefit from the rich visual information in image-label pairs instead of simple visual concepts assigned by their labels. Our Hierarchically Cascaded Transformers, called HCTransformers, is built upon a self-supervised learning framework DINO and is tested on several popular few-shot learning benchmarks. In the inductive setting, HCTransformers surpass the DINO baseline by a large margin of 9.7% 5-way 1-shot accuracy and 9.17% 5-way 5-shot accuracy on mini-ImageNet, which demonstrates HCTransformers are efficient to extract discriminative features. Also, HCTransformers show clear advantages over SOTA few-shot classification methods in both 5-way 1-shot and 5-way 5-shot settings on four popular benchmark datasets, including mini-ImageNet, tiered-ImageNet, FC100, and CIFAR-FS. The trained weights and codes are available at https://github.com/StomachCold/HCTransformers. | https://openaccess.thecvf.com/content/CVPR2022/papers/He_Attribute_Surrogates_Learning_and_Spectral_Tokens_Pooling_in_Transformers_for_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/He_Attribute_Surrogates_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09064 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/He_Attribute_Surrogates_Learning_and_Spectral_Tokens_Pooling_in_Transformers_for_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/He_Attribute_Surrogates_Learning_and_Spectral_Tokens_Pooling_in_Transformers_for_CVPR_2022_paper.html | CVPR 2022 | null |
Generalized Category Discovery | Sagar Vaze, Kai Han, Andrea Vedaldi, Andrew Zisserman | In this paper, we consider a highly general image recognition setting wherein, given a labelled and unlabelled set of images, the task is to categorize all images in the unlabelled set. Here, the unlabelled images may come from labelled classes or from novel ones. Existing recognition methods are not able to deal with this setting, because they make several restrictive assumptions, such as the unlabelled instances only coming from known -- or unknown -- classes, and the number of unknown classes being known a-priori. We address the more unconstrained setting, naming it 'Generalized Category Discovery', and challenge all these assumptions. We first establish strong baselines by taking state-of-the-art algorithms from novel category discovery and adapting them for this task. Next, we propose the use of vision transformers with contrastive representation learning for this open-world setting. We then introduce a simple yet effective semi-supervised k-means method to cluster the unlabelled data into seen and unseen classes automatically, substantially outperforming the baselines. Finally, we also propose a new approach to estimate the number of classes in the unlabelled data. We thoroughly evaluate our approach on public datasets for generic object classification and on fine-grained datasets, leveraging the recent Semantic Shift Benchmark suite. Code: https://www.robots.ox.ac.uk/~vgg/research/gcd | https://openaccess.thecvf.com/content/CVPR2022/papers/Vaze_Generalized_Category_Discovery_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Vaze_Generalized_Category_Discovery_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.02609 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Vaze_Generalized_Category_Discovery_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Vaze_Generalized_Category_Discovery_CVPR_2022_paper.html | CVPR 2022 | null |
Maximum Consensus by Weighted Influences of Monotone Boolean Functions | Erchuan Zhang, David Suter, Ruwan Tennakoon, Tat-Jun Chin, Alireza Bab-Hadiashar, Giang Truong, Syed Zulqarnain Gilani | Maximisation of Consensus (MaxCon) is one of the most widely used robust criteria in computer vision. Tennakoon et al. (CVPR2021), made a connection between MaxCon and estimation of influences of a Monotone Boolean function. In such, there are two distributions involved: the distribution defining the influence measure; and the distribution used for sampling to estimate the influence measure. This paper studies the concept of weighted influences for solving MaxCon. In particular, we study the Bernoulli measures. Theoretically, we prove the weighted influences, under this measure, of points belonging to larger structures are smaller than those of points belonging to smaller structures in general. We also consider another "natural" family of weighting strategies: sampling with uniform measure concentrated on a particular (Hamming) level of the cube. One can choose to have matching distributions: the same for defining the measure as for implementing the sampling. This has the advantage that the sampler is an unbiased estimator of the measure. Based on weighted sampling, we modify the algorithm of Tennakoon et al., and test on both synthetic and real datasets. We show some modest gains of Bernoulli sampling, and we illuminate some of the interactions between structure in data and weighted measures and weighted sampling. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Maximum_Consensus_by_Weighted_Influences_of_Monotone_Boolean_Functions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Maximum_Consensus_by_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.00953 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Maximum_Consensus_by_Weighted_Influences_of_Monotone_Boolean_Functions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Maximum_Consensus_by_Weighted_Influences_of_Monotone_Boolean_Functions_CVPR_2022_paper.html | CVPR 2022 | null |
TransforMatcher: Match-to-Match Attention for Semantic Correspondence | Seungwook Kim, Juhong Min, Minsu Cho | Establishing correspondences between images remains a challenging task, especially under large appearance changes due to different viewpoints or intra-class variations. In this work, we introduce a strong semantic image matching learner, dubbed TransforMatcher, which builds on the success of transformer networks in vision domains. Unlike existing convolution- or attention-based schemes for correspondence, TransforMatcher performs global match-to-match attention for precise match localization and dynamic refinement. To handle a large number of matches in a dense correlation map, we develop a light-weight attention architecture to consider the global match-to-match interactions. We also propose to utilize a multi-channel correlation map for refinement, treating the multi-level scores as features instead of a single score to fully exploit the richer layer-wise semantics. In experiments, TransforMatcher sets a new state of the art on SPair-71k while performing on par with existing SOTA methods on the PF-PASCAL dataset. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_TransforMatcher_Match-to-Match_Attention_for_Semantic_Correspondence_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_TransforMatcher_Match-to-Match_Attention_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.11634 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_TransforMatcher_Match-to-Match_Attention_for_Semantic_Correspondence_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_TransforMatcher_Match-to-Match_Attention_for_Semantic_Correspondence_CVPR_2022_paper.html | CVPR 2022 | null |
Robust Outlier Detection by De-Biasing VAE Likelihoods | Kushal Chauhan, Barath Mohan U, Pradeep Shenoy, Manish Gupta, Devarajan Sridharan | Deep networks often make confident, yet, incorrect, predictions when tested with outlier data that is far removed from their training distributions. Likelihoods computed by deep generative models (DGMs) are a candidate metric for outlier detection with unlabeled data. Yet, previous studies have shown that DGM likelihoods are unreliable and can be easily biased by simple transformations to input data. Here, we examine outlier detection with variational autoencoders (VAEs), among the simplest of DGMs. We propose novel analytical and algorithmic approaches to ameliorate key biases with VAE likelihoods. Our bias corrections are sample-specific, computationally inexpensive, and readily computed for various decoder visible distributions. Next, we show that a well-known image pre-processing technique -- contrast stretching -- extends the effectiveness of bias correction to further improve outlier detection. Our approach achieves state-of-the-art accuracies with nine grayscale and natural image datasets, and demonstrates significant advantages -- both with speed and performance -- over four recent, competing approaches. In summary, lightweight remedies suffice to achieve robust outlier detection with VAEs. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chauhan_Robust_Outlier_Detection_by_De-Biasing_VAE_Likelihoods_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chauhan_Robust_Outlier_Detection_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2108.08760 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chauhan_Robust_Outlier_Detection_by_De-Biasing_VAE_Likelihoods_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chauhan_Robust_Outlier_Detection_by_De-Biasing_VAE_Likelihoods_CVPR_2022_paper.html | CVPR 2022 | null |
Contour-Hugging Heatmaps for Landmark Detection | James McCouat, Irina Voiculescu | We propose an effective and easy-to-implement method for simultaneously performing landmark detection in images and obtaining an ingenious uncertainty measurement for each landmark. Uncertainty measurements for landmarks are particularly useful in medical imaging applications: rather than giving an erroneous reading, a landmark detection system is more useful when it flags its level of confidence in its prediction. When an automated system is unsure of its predictions, the accuracy of the results can be further improved manually by a human. In the medical domain, being able to review an automated system's level of certainty significantly improves a clinician's trust in it. This paper obtains landmark predictions with uncertainty measurements using a three stage method: 1) We train our network on one-hot heatmap images, 2) We calibrate the uncertainty of the network using temperature scaling, 3) We calculate a novel statistic called 'Expected Radial Error' to obtain uncertainty measurements. We find that this method not only achieves localisation results on par with other state-of-the-art methods but also an uncertainty score which correlates with the true error for each landmark thereby bringing an overall step change in what a generic computer vision method for landmark detection should be capable of. In addition, we show that our uncertainty measurement can be used to classify, with good accuracy, what landmark predictions are likely to be inaccurate. Code available at: https://github.com/jfm15/ContourHuggingHeatmaps.git | https://openaccess.thecvf.com/content/CVPR2022/papers/McCouat_Contour-Hugging_Heatmaps_for_Landmark_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/McCouat_Contour-Hugging_Heatmaps_for_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/McCouat_Contour-Hugging_Heatmaps_for_Landmark_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/McCouat_Contour-Hugging_Heatmaps_for_Landmark_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Voxel Field Fusion for 3D Object Detection | Yanwei Li, Xiaojuan Qi, Yukang Chen, Liwei Wang, Zeming Li, Jian Sun, Jiaya Jia | In this work, we present a conceptually simple yet effective framework for cross-modality 3D object detection, named voxel field fusion. The proposed approach aims to maintain cross-modality consistency by representing and fusing augmented image features as a ray in the voxel field. To this end, the learnable sampler is first designed to sample vital features from the image plane that are projected to the voxel grid in a point-to-ray manner, which maintains the consistency in feature representation with spatial context. In addition, ray-wise fusion is conducted to fuse features with the supplemental context in the constructed voxel field. We further develop mixed augmentor to align feature-variant transformations, which bridges the modality gap in data augmentation. The proposed framework is demonstrated to achieve consistent gains in various benchmarks and outperforms previous fusion-based methods on KITTI and nuScenes datasets. Code is made available at https://github.com/dvlab-research/VFF. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Voxel_Field_Fusion_for_3D_Object_Detection_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2205.15938 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Voxel_Field_Fusion_for_3D_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Voxel_Field_Fusion_for_3D_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Divide and Conquer: Compositional Experts for Generalized Novel Class Discovery | Muli Yang, Yuehua Zhu, Jiaping Yu, Aming Wu, Cheng Deng | In response to the explosively-increasing requirement of annotated data, Novel Class Discovery (NCD) has emerged as a promising alternative to automatically recognize unknown classes without any annotation. To this end, a model makes use of a base set to learn basic semantic discriminability that can be transferred to recognize novel classes. Most existing works handle the base and novel sets using separate objectives within a two-stage training paradigm. Despite showing competitive performance on novel classes, they fail to generalize to recognizing samples from both base and novel sets. In this paper, we focus on this generalized setting of NCD (GNCD), and propose to divide and conquer it with two groups of Compositional Experts (ComEx). Each group of experts is designed to characterize the whole dataset in a comprehensive yet complementary fashion. With their union, we can solve GNCD in an efficient end-to-end manner. We further look into the drawback in current NCD methods, and propose to strengthen ComEx with global-to-local and local-to-local regularization. ComEx is evaluated on four popular benchmarks, showing clear superiority towards the goal of GNCD. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Divide_and_Conquer_Compositional_Experts_for_Generalized_Novel_Class_Discovery_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Divide_and_Conquer_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Divide_and_Conquer_Compositional_Experts_for_Generalized_Novel_Class_Discovery_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Divide_and_Conquer_Compositional_Experts_for_Generalized_Novel_Class_Discovery_CVPR_2022_paper.html | CVPR 2022 | null |
Programmatic Concept Learning for Human Motion Description and Synthesis | Sumith Kulal, Jiayuan Mao, Alex Aiken, Jiajun Wu | We introduce Programmatic Motion Concepts, a hierarchical motion representation for human actions that captures both low level motion and high level description as motion concepts. This representation enables human motion description, interactive editing, and controlled synthesis of novel video sequences within a single framework. We present an architecture that learns this concept representation from paired video and action sequences in a semi-supervised manner. The compactness of our representation also allows us to present a low-resource training recipe for data-efficient learning. By outperforming established baselines, especially in small data regime, we demonstrate the efficiency and effectiveness of our framework for multiple applications. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kulal_Programmatic_Concept_Learning_for_Human_Motion_Description_and_Synthesis_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kulal_Programmatic_Concept_Learning_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kulal_Programmatic_Concept_Learning_for_Human_Motion_Description_and_Synthesis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kulal_Programmatic_Concept_Learning_for_Human_Motion_Description_and_Synthesis_CVPR_2022_paper.html | CVPR 2022 | null |
Interpretable Part-Whole Hierarchies and Conceptual-Semantic Relationships in Neural Networks | Nicola Garau, Niccolò Bisagno, Zeno Sambugaro, Nicola Conci | Deep neural networks achieve outstanding results in a large variety of tasks, often outperforming human experts. However, a known limitation of current neural architectures is the poor accessibility to understand and interpret the network response to a given input. This is directly related to the huge number of variables and the associated non-linearities of neural models, which are often used as black boxes. When it comes to critical applications as autonomous driving, security and safety, medicine and health, the lack of interpretability of the network behavior tends to induce skepticism and limited trustworthiness, despite the accurate performance of such systems in the given task. Furthermore, a single metric, such as the classification accuracy, provides a non-exhaustive evaluation of most real-world scenarios. In this paper, we want to make a step forward towards interpretability in neural networks, providing new tools to interpret their behavior. We present Agglomerator, a framework capable of providing a representation of part-whole hierarchies from visual cues and organizing the input distribution matching the conceptual-semantic hierarchical structure between classes. We evaluate our method on common datasets, such as SmallNORB, MNIST, FashionMNIST, CIFAR-10, and CIFAR-100, providing a more interpretable model than other state-of-the-art approaches. | https://openaccess.thecvf.com/content/CVPR2022/papers/Garau_Interpretable_Part-Whole_Hierarchies_and_Conceptual-Semantic_Relationships_in_Neural_Networks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Garau_Interpretable_Part-Whole_Hierarchies_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.03282 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Garau_Interpretable_Part-Whole_Hierarchies_and_Conceptual-Semantic_Relationships_in_Neural_Networks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Garau_Interpretable_Part-Whole_Hierarchies_and_Conceptual-Semantic_Relationships_in_Neural_Networks_CVPR_2022_paper.html | CVPR 2022 | null |
Fast Algorithm for Low-Rank Tensor Completion in Delay-Embedded Space | Ryuki Yamamoto, Hidekata Hontani, Akira Imakura, Tatsuya Yokota | Tensor completion using multiway delay-embedding transform (MDT) (or Hankelization) suffers from the large memory requirement and high computational cost in spite of its high potentiality for the image modeling. Recent studies have shown high completion performance with a relatively small window size, but experiments with large window sizes require huge amount of memory and cannot be easily calculated. In this study, we address this serious computational issue, and propose its fast and efficient algorithm. Key techniques of the proposed method are based on two properties: (1) the signal after MDT can be diagonalized by Fourier transform, (2) an inverse MDT can be represented as a convolutional form. To use the properties, we modify MDT-Tucker, a method using Tucker decomposition with MDT, and introducing the fast and efficient algorithm. Our experiments show more than 100 times acceleration while maintaining high accuracy, and to realize the computation with large window size. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yamamoto_Fast_Algorithm_for_Low-Rank_Tensor_Completion_in_Delay-Embedded_Space_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yamamoto_Fast_Algorithm_for_Low-Rank_Tensor_Completion_in_Delay-Embedded_Space_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yamamoto_Fast_Algorithm_for_Low-Rank_Tensor_Completion_in_Delay-Embedded_Space_CVPR_2022_paper.html | CVPR 2022 | null |
Panoptic, Instance and Semantic Relations: A Relational Context Encoder To Enhance Panoptic Segmentation | Shubhankar Borse, Hyojin Park, Hong Cai, Debasmit Das, Risheek Garrepalli, Fatih Porikli | This paper presents a novel framework to integrate both semantic and instance contexts for panoptic segmentation. In existing works, it is common to use a shared backbone to extract features for both things (countable classes such as vehicles) and stuff (uncountable classes such as roads). This, however, fails to capture the rich relations among them, which can be utilized to enhance visual understanding and segmentation performance. To address this shortcoming, we propose a novel Panoptic, Instance, and Semantic Relations (PISR) module to exploit such contexts. First, we generate panoptic encodings to summarize key features of the semantic classes and predicted instances. A Panoptic Relational Attention (PRA) module is then applied to the encodings and the global feature map from the backbone. It produces a feature map that captures 1) the relations across semantic classes and instances and 2) the relations between these panoptic categories and spatial features. PISR also automatically learns to focus on the more important instances, making it robust to the number of instances used in the relational attention module. Moreover, PISR is a general module that can be applied to any existing panoptic segmentation architecture. Through extensive evaluations on panoptic segmentation benchmarks like Cityscapes, COCO, and ADE20K, we show that PISR attains considerable improvements over existing approaches. | https://openaccess.thecvf.com/content/CVPR2022/papers/Borse_Panoptic_Instance_and_Semantic_Relations_A_Relational_Context_Encoder_To_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Borse_Panoptic_Instance_and_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.05370 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Borse_Panoptic_Instance_and_Semantic_Relations_A_Relational_Context_Encoder_To_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Borse_Panoptic_Instance_and_Semantic_Relations_A_Relational_Context_Encoder_To_CVPR_2022_paper.html | CVPR 2022 | null |
Point2Seq: Detecting 3D Objects As Sequences | Yujing Xue, Jiageng Mao, Minzhe Niu, Hang Xu, Michael Bi Mi, Wei Zhang, Xiaogang Wang, Xinchao Wang | We present a simple and effective framework, named Point2Seq, for 3D object detection from point clouds. In contrast to previous methods that normally predict attributes of 3D objects all at once, we expressively model the interdependencies between attributes of 3D objects, which in turn enables a better detection accuracy. Specifically, we view each 3D object as a sequence of words and reformulate the 3D object detection task as decoding words from 3D scenes in an auto-regressive manner. We further propose a lightweight scene-to-sequence decoder that can auto-regressively generate words conditioned on features from a 3D scene as well as cues from the preceding words. The predicted words eventually constitute a set of sequences that completely describe the 3D objects in the scene, and all the predicted sequences are then automatically assigned to the respective ground truths through similarity-based sequence matching. Our approach is conceptually intuitive and can be readily plugged upon most existing 3D-detection backbones without adding too much computational overhead; the sequential decoding paradigm we proposed, on the other hand, can better exploit information from complex 3D scenes with the aid of preceding predicted words. Without bells and whistles, our method significantly outperforms the previous anchor- and center-based 3D object detection frameworks, yielding the new state-of-the-art on the challenging ONCE dataset as well as the Waymo Open Dataset. Code will be made publicly available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xue_Point2Seq_Detecting_3D_Objects_As_Sequences_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xue_Point2Seq_Detecting_3D_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.13394 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xue_Point2Seq_Detecting_3D_Objects_As_Sequences_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xue_Point2Seq_Detecting_3D_Objects_As_Sequences_CVPR_2022_paper.html | CVPR 2022 | null |
Less Is More: Generating Grounded Navigation Instructions From Landmarks | Su Wang, Ceslee Montgomery, Jordi Orbay, Vighnesh Birodkar, Aleksandra Faust, Izzeddin Gur, Natasha Jaques, Austin Waters, Jason Baldridge, Peter Anderson | We study the automatic generation of navigation instructions from 360-degree images captured on indoor routes. Existing generators suffer from poor visual grounding, causing them to rely on language priors and hallucinate objects. Our MARKY-MT5 system addresses this by focusing on visual landmarks; it comprises a first stage landmark detector and a second stage generator--a multimodal, multilingual, multitask encoder-decoder. To train it, we bootstrap grounded landmark annotations on top of the Room-across-Room (RxR) dataset. Using text parsers, weak supervision from RxR's pose traces, and a multilingual image-text encoder trained on 1.8b images, we identify 1.1m English, Hindi and Telugu landmark descriptions and ground them to specific regions in panoramas. On Room-to-Room, human wayfinders obtain success rates (SR) of 73% following MARKY-MT5's instructions, just shy of their 76% SR following human instructions---and well above SRs with other generators. Evaluations on RxR's longer, diverse paths obtain 62-64% SRs on three languages. Generating such high-quality navigation instructions in novel environments is a step towards conversational navigation tools and could facilitate larger-scale training of instruction-following agents. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Less_Is_More_Generating_Grounded_Navigation_Instructions_From_Landmarks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Less_Is_More_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.12872 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Less_Is_More_Generating_Grounded_Navigation_Instructions_From_Landmarks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Less_Is_More_Generating_Grounded_Navigation_Instructions_From_Landmarks_CVPR_2022_paper.html | CVPR 2022 | null |
Task-Adaptive Negative Envision for Few-Shot Open-Set Recognition | Shiyuan Huang, Jiawei Ma, Guangxing Han, Shih-Fu Chang | We study the problem of few-shot open-set recognition (FSOR), which learns a recognition system capable of both fast adaptation to new classes with limited labeled examples and rejection of unknown negative samples. Traditional large-scale open-set methods have been shown ineffective for FSOR problem due to data limitation. Current FSOR methods typically calibrate few-shot closed-set classifiers to be sensitive to negative samples so that they can be rejected via thresholding. However, threshold tuning is a challenging process as different FSOR tasks may require different rejection powers. In this paper, we instead propose task-adaptive negative class envision for FSOR to integrate threshold tuning into the learning process. Specifically, we augment the few-shot closed-set classifier with additional negative prototypes generated from few-shot examples. By incorporating few-shot class correlations in the negative generation process, we are able to learn dynamic rejection boundaries for FSOR tasks. Besides, we extend our method to generalized few-shot open-set recognition (GFSOR), which requires classification on both many-shot and few-shot classes as well as rejection of negative samples. Extensive experiments on public benchmarks validate our methods on both problems. | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Task-Adaptive_Negative_Envision_for_Few-Shot_Open-Set_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_Task-Adaptive_Negative_Envision_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Task-Adaptive_Negative_Envision_for_Few-Shot_Open-Set_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Task-Adaptive_Negative_Envision_for_Few-Shot_Open-Set_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
DisARM: Displacement Aware Relation Module for 3D Detection | Yao Duan, Chenyang Zhu, Yuqing Lan, Renjiao Yi, Xinwang Liu, Kai Xu | We introduce Displacement Aware Relation Module (DisARM), a novel neural network module for enhancing the performance of 3D object detection in point cloud scenes. The core idea is extracting the most principal contextual information is critical for detection while the target is incomplete or featureless. We find that relations between proposals provide a good representation to describe the context. However, adopting relations between all the object or patch proposals for detection is inefficient, and an imbalanced combination of local and global relations brings extra noise that could mislead the training. Rather than working with all relations, we find that training with relations only between the most representative ones, or anchors, can significantly boost the detection performance. Good anchors should be semantic-aware with no ambiguity and able to describe the whole layout of a scene with no redundancy. To find the anchors, we first perform a preliminary relation anchor module with an objectness-aware sampling approach and then devise a displacement based module for weighing the relation importance for better utilization of contextual information. This light-weight relation module leads to significantly higher accuracy of object instance detection when being plugged into the state-of- the-art detectors. Evaluations on the public benchmarks of real-world scenes show that our method achieves the state-of-the-art performance on both SUN RGB-D and ScanNet V2. The code and models are publicly available at https://github.com/YaraDuan/DisARM. | https://openaccess.thecvf.com/content/CVPR2022/papers/Duan_DisARM_Displacement_Aware_Relation_Module_for_3D_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Duan_DisARM_Displacement_Aware_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01152 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Duan_DisARM_Displacement_Aware_Relation_Module_for_3D_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Duan_DisARM_Displacement_Aware_Relation_Module_for_3D_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
ETHSeg: An Amodel Instance Segmentation Network and a Real-World Dataset for X-Ray Waste Inspection | Lingteng Qiu, Zhangyang Xiong, Xuhao Wang, Kenkun Liu, Yihan Li, Guanying Chen, Xiaoguang Han, Shuguang Cui | Waste inspection for packaged waste is an important step in the pipeline of waste disposal. Previous methods either rely on manual visual checking or RGB image-based inspection algorithm, requiring costly preparation procedures (e.g., open the bag and spread the waste items). Moreover, occluded items are very likely to be left out. Inspired by the fact that X-ray has a strong penetrating power to see through the bag and overlapping objects, we propose to perform waste inspection efficiently using X-ray images without the need to open the bag. We introduce a novel problem of instance-level waste segmentation in X-ray image for intelligent waste inspection, and contribute a real dataset consisting of 5,038 X-ray images (totally 30,881 waste items) with high-quality annotations (i.e., waste categories, object boxes, and instance-level masks) as a benchmark for this problem. As existing segmentation methods are mainly designed for natural images and cannot take advantage of the characteristics of X-ray waste images (e.g., heavy occlusions and penetration effect), we propose a new instance segmentation method to explicitly take these image characteristics into account. Specifically, our method adopts an easy-to-hard disassembling strategy to use high confidence predictions to guide the segmentation of highly overlapped objects, and a global structure guidance module to better capture the complex contour information caused by the penetration effect. Extensive experiments demonstrate the effectiveness of the proposed method. Our dataset is released at WIXRayNet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Qiu_ETHSeg_An_Amodel_Instance_Segmentation_Network_and_a_Real-World_Dataset_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Qiu_ETHSeg_An_Amodel_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Qiu_ETHSeg_An_Amodel_Instance_Segmentation_Network_and_a_Real-World_Dataset_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Qiu_ETHSeg_An_Amodel_Instance_Segmentation_Network_and_a_Real-World_Dataset_CVPR_2022_paper.html | CVPR 2022 | null |
MixFormer: Mixing Features Across Windows and Dimensions | Qiang Chen, Qiman Wu, Jian Wang, Qinghao Hu, Tao Hu, Errui Ding, Jian Cheng, Jingdong Wang | While local-window self-attention performs notably in vision tasks, it suffers from limited receptive field and weak modeling capability issues. This is mainly because it performs self-attention within non-overlapped windows and shares weights on the channel dimension. We propose MixFormer to find a solution. First, we combine local-window self-attention with depth-wise convolution in a parallel design, modeling cross-window connections to enlarge the receptive fields. Second, we propose bi-directional interactions across branches to provide complementary clues in the channel and spatial dimensions. These two designs are integrated to achieve efficient feature mixing among windows and dimensions. Our MixFormer provides competitive results on image classification with EfficientNet and shows better results than RegNet and Swin Transformer. Performance in downstream tasks outperforms its alternatives by significant margins with less computational costs in 5 dense prediction tasks on MS COCO, ADE20k, and LVIS. Code is available at https://github.com/PaddlePaddle/PaddleClas. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_MixFormer_Mixing_Features_Across_Windows_and_Dimensions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_MixFormer_Mixing_Features_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.02557 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_MixFormer_Mixing_Features_Across_Windows_and_Dimensions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_MixFormer_Mixing_Features_Across_Windows_and_Dimensions_CVPR_2022_paper.html | CVPR 2022 | null |
Killing Two Birds With One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC | Xiang An, Jiankang Deng, Jia Guo, Ziyong Feng, XuHan Zhu, Jing Yang, Tongliang Liu | Learning discriminative deep feature embeddings by using million-scale in-the-wild datasets and margin-based softmax loss is the current state-of-the-art approach for face recognition. However, the memory and computing cost of the Fully Connected (FC) layer linearly scales up to the number of identities in the training set. Besides, the large-scale training data inevitably suffers from inter-class conflict and long-tailed distribution. In this paper, we propose a sparsely updating variant of the FC layer, named Partial FC (PFC). In each iteration, positive class centers and a random subset of negative class centers are selected to compute the margin-based softmax loss. All class centers are still maintained throughout the whole training process, but only a subset is selected and updated in each iteration. Therefore, the computing requirement, the probability of inter-class conflict, and the frequency of passive update on tail class centers, are dramatically reduced. Extensive experiments across different training data and backbones (e.g. CNN and ViT) confirm the effectiveness, robustness and efficiency of the proposed PFC. | https://openaccess.thecvf.com/content/CVPR2022/papers/An_Killing_Two_Birds_With_One_Stone_Efficient_and_Robust_Training_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.15565 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/An_Killing_Two_Birds_With_One_Stone_Efficient_and_Robust_Training_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/An_Killing_Two_Birds_With_One_Stone_Efficient_and_Robust_Training_CVPR_2022_paper.html | CVPR 2022 | null |
NeRF-Editing: Geometry Editing of Neural Radiance Fields | Yu-Jie Yuan, Yang-Tian Sun, Yu-Kun Lai, Yuewen Ma, Rongfei Jia, Lin Gao | Implicit neural rendering, especially Neural Radiance Field (NeRF), has shown great potential in novel view synthesis of a scene. However, current NeRF-based methods cannot enable users to perform user-controlled shape deformation in the scene. While existing works have proposed some approaches to modify the radiance field according to the user's constraints, the modification is limited to color editing or object translation and rotation. In this paper, we propose a method that allows users to perform controllable shape deformation on the implicit representation of the scene, and synthesizes the novel view images of the edited scene without re-training the network. Specifically, we establish a correspondence between the extracted explicit mesh representation and the implicit neural representation of the target scene. Users can first utilize well-developed mesh-based deformation methods to deform the mesh representation of the scene. Our method then utilizes user edits from the mesh representation to bend the camera rays by introducing a tetrahedra mesh as a proxy, obtaining the rendering results of the edited scene. Extensive experiments demonstrate that our framework can achieve ideal editing results not only on synthetic data, but also on real scenes captured by users. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yuan_NeRF-Editing_Geometry_Editing_of_Neural_Radiance_Fields_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yuan_NeRF-Editing_Geometry_Editing_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yuan_NeRF-Editing_Geometry_Editing_of_Neural_Radiance_Fields_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yuan_NeRF-Editing_Geometry_Editing_of_Neural_Radiance_Fields_CVPR_2022_paper.html | CVPR 2022 | null |
Optimal Correction Cost for Object Detection Evaluation | Mayu Otani, Riku Togashi, Yuta Nakashima, Esa Rahtu, Janne Heikkilä, Shin'ichi Satoh | Mean Average Precision (mAP) is the primary evaluation measure for object detection. Although object detection has a broad range of applications, mAP evaluates detectors in terms of the performance of ranked instance retrieval. Such the assumption for the evaluation task does not suit some downstream tasks. To alleviate the gap between downstream tasks and the evaluation scenario, we propose Optimal Correction Cost (OC-cost), which assesses detection accuracy at image level. OC-cost computes the cost of correcting detections to ground truths as a measure of accuracy. The cost is obtained by solving an optimal transportation problem between the detections and the ground truths. Unlike mAP, OC-cost is designed to penalize false positive and false negative detections properly, and every image in a dataset is treated equally. Our experimental result validates that OC-cost has better agreement with human preference than a ranking-based measure, i.e., mAP for a single image. We also show that detectors' rankings by OC-cost are more consistent on different data splits than mAP. Our goal is not to replace mAP with OC-cost but provide an additional tool to evaluate detectors from another aspect. To help future researchers and developers choose a target measure, we provide a series of experiments to clarify how mAP and OC-cost differ. | https://openaccess.thecvf.com/content/CVPR2022/papers/Otani_Optimal_Correction_Cost_for_Object_Detection_Evaluation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Otani_Optimal_Correction_Cost_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.14438 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Otani_Optimal_Correction_Cost_for_Object_Detection_Evaluation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Otani_Optimal_Correction_Cost_for_Object_Detection_Evaluation_CVPR_2022_paper.html | CVPR 2022 | null |
Contextual Similarity Distillation for Asymmetric Image Retrieval | Hui Wu, Min Wang, Wengang Zhou, Houqiang Li, Qi Tian | Asymmetric image retrieval, which typically uses small model for query side and large model for database server, is an effective solution for resource-constrained scenarios. However, existing approaches either fail to achieve feature coherence or make strong assumptions, e.g., requiring labeled datasets or classifiers from large model, etc., which limits their practical application. To this end, we propose a flexible contextual similarity distillation framework to enhance the small query model and keep its output feature compatible with that of large gallery model, which is crucial with asymmetric retrieval. In our approach, we learn the small model with a new contextual similarity consistency constraint without any data label. During the small model learning, it preserves the contextual similarity among each training image and its neighbors with the features extracted by the large model. Note that this simple constraint is consistent with simultaneous first-order feature vector preserving and second-order ranking list preserving. Extensive experiments show that the proposed method outperforms the state-of-the-art methods on the Revisited Oxford and Paris datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Contextual_Similarity_Distillation_for_Asymmetric_Image_Retrieval_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Contextual_Similarity_Distillation_for_Asymmetric_Image_Retrieval_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Contextual_Similarity_Distillation_for_Asymmetric_Image_Retrieval_CVPR_2022_paper.html | CVPR 2022 | null |
FineDiving: A Fine-Grained Dataset for Procedure-Aware Action Quality Assessment | Jinglin Xu, Yongming Rao, Xumin Yu, Guangyi Chen, Jie Zhou, Jiwen Lu | Most existing action quality assessment methods rely on the deep features of an entire video to predict the score, which is less reliable due to the non-transparent inference process and poor interpretability. We argue that understanding both high-level semantics and internal temporal structures of actions in competitive sports videos is the key to making predictions accurate and interpretable. Towards this goal, we construct a new fine-grained dataset, called FineDiving, developed on diverse diving events with detailed annotations on action procedures. We also propose a procedure-aware approach for action quality assessment, learned by a new Temporal Segmentation Attention module. Specifically, we propose to parse pairwise query and exemplar action instances into consecutive steps with diverse semantic and temporal correspondences. The procedure-aware cross-attention is proposed to learn embeddings between query and exemplar steps to discover their semantic, spatial, and temporal correspondences, and further serve for fine-grained contrastive regression to derive a reliable scoring mechanism. Extensive experiments demonstrate that our approach achieves substantial improvements over the state-of-the-art methods with better interpretability. The dataset and code are available at https://github.com/xujinglin/FineDiving. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_FineDiving_A_Fine-Grained_Dataset_for_Procedure-Aware_Action_Quality_Assessment_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_FineDiving_A_Fine-Grained_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.03646 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_FineDiving_A_Fine-Grained_Dataset_for_Procedure-Aware_Action_Quality_Assessment_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_FineDiving_A_Fine-Grained_Dataset_for_Procedure-Aware_Action_Quality_Assessment_CVPR_2022_paper.html | CVPR 2022 | null |
Artistic Style Discovery With Independent Components | Xin Xie, Yi Li, Huaibo Huang, Haiyan Fu, Wanwan Wang, Yanqing Guo | Style transfer has been well studied in recent years with excellent performance processed. While existing methods usually choose CNNs as the powerful tool to accomplish superb stylization, less attention was paid to the latent style space. Rare exploration of underlying dimensions results in the poor style controllability and the limited practical application. In this work, we rethink the internal meaning of style features, further proposing a novel unsupervised algorithm for style discovery and achieving personalized manipulation. In particular, we take a closer look into the mechanism of style transfer and obtain different artistic style components from the latent space consisting of different style features. Then fresh styles can be generated by linear combination according to various style components. Experimental results have shown that our approach is superb in 1) restylizing the original output with the diverse artistic styles discovered from the latent space while keeping the content unchanged, and 2) being generic and compatible for various style transfer methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xie_Artistic_Style_Discovery_With_Independent_Components_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_Artistic_Style_Discovery_With_Independent_Components_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_Artistic_Style_Discovery_With_Independent_Components_CVPR_2022_paper.html | CVPR 2022 | null |
HEAT: Holistic Edge Attention Transformer for Structured Reconstruction | Jiacheng Chen, Yiming Qian, Yasutaka Furukawa | This paper presents a novel attention-based neural network for structured reconstruction, which takes a 2D raster image as an input and reconstructs a planar graph depicting an underlying geometric structure. The approach detects corners and classifies edge candidates between corners in an end-to-end manner. Our contribution is a holistic edge classification architecture, which 1) initializes the feature of an edge candidate by a trigonometric positional encoding of its end-points; 2) fuses image feature to each edge candidate by deformable attention; 3) employs two weight-sharing Transformer decoders to learn holistic structural patterns over the graph edge candidates; and 4) is trained with a masked learning strategy. The corner detector is a variant of the edge classification architecture, adapted to operate on pixels as corner candidates. We conduct experiments on two structured reconstruction tasks: outdoor building architecture and indoor floorplan planar graph reconstruction. Extensive qualitative and quantitative evaluations demonstrate the superiority of our approach over the state of the art. Code and pre-trained models are available at https://heat-structured-reconstruction.github.io | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_HEAT_Holistic_Edge_Attention_Transformer_for_Structured_Reconstruction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_HEAT_Holistic_Edge_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.15143 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_HEAT_Holistic_Edge_Attention_Transformer_for_Structured_Reconstruction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_HEAT_Holistic_Edge_Attention_Transformer_for_Structured_Reconstruction_CVPR_2022_paper.html | CVPR 2022 | null |
HyperStyle: StyleGAN Inversion With HyperNetworks for Real Image Editing | Yuval Alaluf, Omer Tov, Ron Mokady, Rinon Gal, Amit Bermano | The inversion of real images into StyleGAN's latent space is a well-studied problem. Nevertheless, applying existing approaches to real-world scenarios remains an open challenge, due to an inherent trade-off between reconstruction and editability: latent space regions which can accurately represent real images typically suffer from degraded semantic control. Recent work proposes to mitigate this trade-off by fine-tuning the generator to add the target image to well-behaved, editable regions of the latent space. While promising, this fine-tuning scheme is impractical for prevalent use as it requires a lengthy training phase for each new image. In this work, we introduce this approach into the realm of encoder-based inversion. We propose HyperStyle, a hypernetwork that learns to modulate StyleGAN's weights to faithfully express a given image in editable regions of the latent space. A naive modulation approach would require training a hypernetwork with over three billion parameters. Through careful network design, we reduce this to be in line with existing encoders. HyperStyle yields reconstructions comparable to those of optimization techniques with the near real-time inference capabilities of encoders. Lastly, we demonstrate HyperStyle's effectiveness on several applications beyond the inversion task, including the editing of out-of-domain images which were never seen during training. | https://openaccess.thecvf.com/content/CVPR2022/papers/Alaluf_HyperStyle_StyleGAN_Inversion_With_HyperNetworks_for_Real_Image_Editing_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Alaluf_HyperStyle_StyleGAN_Inversion_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.15666 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Alaluf_HyperStyle_StyleGAN_Inversion_With_HyperNetworks_for_Real_Image_Editing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Alaluf_HyperStyle_StyleGAN_Inversion_With_HyperNetworks_for_Real_Image_Editing_CVPR_2022_paper.html | CVPR 2022 | null |
DASO: Distribution-Aware Semantics-Oriented Pseudo-Label for Imbalanced Semi-Supervised Learning | Youngtaek Oh, Dong-Jin Kim, In So Kweon | The capability of the traditional semi-supervised learning (SSL) methods is far from real-world application due to severely biased pseudo-labels caused by (1) class imbalance and (2) class distribution mismatch between labeled and unlabeled data. This paper addresses such a relatively under-explored problem. First, we propose a general pseudo-labeling framework that class-adaptively blends the semantic pseudo-label from a similarity-based classifier to the linear one from the linear classifier, after making the observation that both types of pseudo-labels have complementary properties in terms of bias. We further introduce a novel semantic alignment loss to establish balanced feature representation to reduce the biased predictions from the classifier. We term the whole framework as Distribution-Aware Semantics-Oriented (DASO) Pseudo-label. We conduct extensive experiments in a wide range of imbalanced benchmarks: CIFAR10/100-LT, STL10-LT, and large-scale long-tailed Semi-Aves with open-set class, and demonstrate that, the proposed DASO framework reliably improves SSL learners with unlabeled data especially when both (1) class imbalance and (2) distribution mismatch dominate. | https://openaccess.thecvf.com/content/CVPR2022/papers/Oh_DASO_Distribution-Aware_Semantics-Oriented_Pseudo-Label_for_Imbalanced_Semi-Supervised_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Oh_DASO_Distribution-Aware_Semantics-Oriented_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.05682 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Oh_DASO_Distribution-Aware_Semantics-Oriented_Pseudo-Label_for_Imbalanced_Semi-Supervised_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Oh_DASO_Distribution-Aware_Semantics-Oriented_Pseudo-Label_for_Imbalanced_Semi-Supervised_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Mobile-Former: Bridging MobileNet and Transformer | Yinpeng Chen, Xiyang Dai, Dongdong Chen, Mengchen Liu, Xiaoyi Dong, Lu Yuan, Zicheng Liu | We present Mobile-Former, a parallel design of MobileNet and transformer with a two-way bridge in between. This structure leverages the advantages of MobileNet at local processing and transformer at global interaction. And the bridge enables bidirectional fusion of local and global features. Different from recent works on vision transformer, the transformer in Mobile-Former contains very few tokens (e.g. 6 or fewer tokens) that are randomly initialized to learn global priors, resulting in low computational cost. Combining with the proposed light-weight cross attention to model the bridge, Mobile-Former is not only computationally efficient, but also has more representation power. It outperforms MobileNetV3 at low FLOP regime from 25M to 500M FLOPs on ImageNet classification. For instance, Mobile-Former achieves 77.9% top-1 accuracy at 294M FLOPs, gaining 1.3% over MobileNetV3 but saving 17% of computations. When transferring to object detection, Mobile-Former outperforms MobileNetV3 by 8.6 AP in RetinaNet framework. Furthermore, we build an efficient end-to-end detector by replacing backbone, encoder and decoder in DETR with Mobile-Former, which outperforms DETR by 1.3 AP but saves 52% of computational cost and 36% of parameters. Code will be released at https://github.com/aaboys/mobileformer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Mobile-Former_Bridging_MobileNet_and_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Mobile-Former_Bridging_MobileNet_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Mobile-Former_Bridging_MobileNet_and_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Mobile-Former_Bridging_MobileNet_and_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
Exploiting Pseudo Labels in a Self-Supervised Learning Framework for Improved Monocular Depth Estimation | Andra Petrovai, Sergiu Nedevschi | We present a novel self-distillation based self-supervised monocular depth estimation (SD-SSMDE) learning framework. In the first step, our network is trained in a self-supervised regime on high-resolution images with the photometric loss. The network is further used to generate pseudo depth labels for all the images in the training set. To improve the performance of our estimates, in the second step, we re-train the network with the scale invariant logarithmic loss supervised by pseudo labels. We resolve scale ambiguity and inter-frame scale consistency by introducing an automatically computed scale in our depth labels. To filter out noisy depth values, we devise a filtering scheme based on the 3D consistency between consecutive views. Extensive experiments demonstrate that each proposed component and the self-supervised learning framework improve the quality of the depth estimation over the baseline and achieve state-of-the-art results on the KITTI and Cityscapes datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Petrovai_Exploiting_Pseudo_Labels_in_a_Self-Supervised_Learning_Framework_for_Improved_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Petrovai_Exploiting_Pseudo_Labels_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Petrovai_Exploiting_Pseudo_Labels_in_a_Self-Supervised_Learning_Framework_for_Improved_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Petrovai_Exploiting_Pseudo_Labels_in_a_Self-Supervised_Learning_Framework_for_Improved_CVPR_2022_paper.html | CVPR 2022 | null |
DESTR: Object Detection With Split Transformer | Liqiang He, Sinisa Todorovic | Self- and cross-attention in Transformers provide for high model capacity, making them viable models for object detection. However, Transformers still lag in performance behind CNN-based detectors. This is, we believe, because: (a) Cross-attention is used for both classification and bounding-box regression tasks; (b) Transformer's decoder poorly initializes content queries; and (c) Self-attention poorly accounts for certain prior knowledge which could help improve inductive bias. These limitations are addressed with the corresponding three contributions. First, we propose a new Detection Split Transformer (DESTR) that separates estimation of cross-attention into two independent branches -- one tailored for classification and the other for box regression. Second, we use a mini-detector to initialize the content queries in the decoder with classification and regression embeddings of the respective heads in the mini-detector. Third, we augment self-attention in the decoder to additionally account for pairs of adjacent object queries. Our experiments on the MS-COCO dataset show that DESTR outperforms DETR and its successors. | https://openaccess.thecvf.com/content/CVPR2022/papers/He_DESTR_Object_Detection_With_Split_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/He_DESTR_Object_Detection_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/He_DESTR_Object_Detection_With_Split_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/He_DESTR_Object_Detection_With_Split_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
LTP: Lane-Based Trajectory Prediction for Autonomous Driving | Jingke Wang, Tengju Ye, Ziqing Gu, Junbo Chen | The reasonable trajectory prediction of surrounding traffic participants is crucial for autonomous driving. Especially, how to predict multiple plausible trajectories is still a challenging problem because of the multiple possibilities of the future. Proposal-based prediction methods address the multi-modality issues with a two-stage approach, commonly using intention classification followed by motion regression. This paper proposes a two-stage proposal-based motion forecasting method that exploits the sliced lane segments as fine-grained, shareable, and interpretable proposals. We use Graph neural network and Transformer to encode the shape and interaction information among the map sub-graphs and the agents sub-graphs. In addition, we propose a variance-based non-maximum suppression strategy to select representative trajectories that ensure the diversity of the final output. Experiments on the Argoverse dataset show that the proposed method outperforms state-of-the-art methods, and the lane segments-based proposals as well as the variance-based non-maximum suppression strategy both contribute to the performance improvement. Moreover, we demonstrate that the proposed method can achieve reliable performance with a lower collision rate and fewer off-road scenarios in the closed-loop simulation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_LTP_Lane-Based_Trajectory_Prediction_for_Autonomous_Driving_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_LTP_Lane-Based_Trajectory_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_LTP_Lane-Based_Trajectory_Prediction_for_Autonomous_Driving_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_LTP_Lane-Based_Trajectory_Prediction_for_Autonomous_Driving_CVPR_2022_paper.html | CVPR 2022 | null |
CycleMix: A Holistic Strategy for Medical Image Segmentation From Scribble Supervision | Ke Zhang, Xiahai Zhuang | Curating a large set of fully annotated training data can be costly, especially for the tasks of medical image segmentation. Scribble, a weaker form of annotation, is more obtainable in practice, but training segmentation models from limited supervision of scribbles is still challenging. To address the difficulties, we propose a new framework for scribble learning-based medical image segmentation, which is composed of mix augmentation and cycle consistency and thus is referred to as CycleMix. For augmentation of supervision, CycleMix adopts the mixup strategy with a dedicated design of random occlusion, to perform increments and decrements of scribbles. For regularization of supervision, CycleMix intensifies the training objective with consistency losses to penalize inconsistent segmentation, which results in significant improvement of segmentation performance. Results on two open datasets, i.e., ACDC and MSCMRseg, showed that the proposed method achieved exhilarating performance, demonstrating comparable or even better accuracy than the fully-supervised methods. The code and expert-made scribble annotations for MSCMRseg are publicly available at https://github.com/BWGZK/CycleMIx. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_CycleMix_A_Holistic_Strategy_for_Medical_Image_Segmentation_From_Scribble_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_CycleMix_A_Holistic_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01475 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_CycleMix_A_Holistic_Strategy_for_Medical_Image_Segmentation_From_Scribble_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_CycleMix_A_Holistic_Strategy_for_Medical_Image_Segmentation_From_Scribble_CVPR_2022_paper.html | CVPR 2022 | null |
VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution | Zeyuan Chen, Yinbo Chen, Jingwen Liu, Xingqian Xu, Vidit Goel, Zhangyang Wang, Humphrey Shi, Xiaolong Wang | Videos typically record the streaming and continuous visual data as discrete consecutive frames. Since the storage cost is expensive for videos of high fidelity, most of them are stored in a relatively low resolution and frame rate. Recent works of Space-Time Video Super-Resolution (STVSR) are developed to incorporate temporal interpolation and spatial super-resolution in a unified framework. However, most of them only support a fixed up-sampling scale, which limits their flexibility and applications. In this work, instead of following the discrete representations, we propose Video Implicit Neural Representation (VideoINR), and we show its applications for STVSR. The learned implicit neural representation can be decoded to videos of arbitrary spatial resolution and frame rate. We show that VideoINR achieves competitive performances with state-of-the-art STVSR methods on common up-sampling scales and significantly outperforms prior works on continuous and out-of-training-distribution scales. Our project page is at http://zeyuan-chen.com/VideoINR/ and code is available at https://github.com/Picsart-AI-Research/VideoINR-Continuous-Space-Time-Super-Resolution. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_VideoINR_Learning_Video_Implicit_Neural_Representation_for_Continuous_Space-Time_Super-Resolution_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_VideoINR_Learning_Video_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_VideoINR_Learning_Video_Implicit_Neural_Representation_for_Continuous_Space-Time_Super-Resolution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_VideoINR_Learning_Video_Implicit_Neural_Representation_for_Continuous_Space-Time_Super-Resolution_CVPR_2022_paper.html | CVPR 2022 | null |
Towards End-to-End Unified Scene Text Detection and Layout Analysis | Shangbang Long, Siyang Qin, Dmitry Panteleev, Alessandro Bissacco, Yasuhisa Fujii, Michalis Raptis | Scene text detection and document layout analysis have long been treated as two separate tasks in different image domains. In this paper, we bring them together and introduce the task of unified scene text detection and layout analysis. The first hierarchical scene text dataset is introduced to enable this novel research task. We also propose a novel method that is able to simultaneously detect scene text and form text clusters in a unified way. Comprehensive experiments show that our unified model achieves better performance than multiple well-designed baseline methods. Additionally, this model achieves state-of-the-art results on multiple scene text detection datasets without the need of complex post-processing. Dataset and code: https://github.com/google-research-datasets/hiertext. | https://openaccess.thecvf.com/content/CVPR2022/papers/Long_Towards_End-to-End_Unified_Scene_Text_Detection_and_Layout_Analysis_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Long_Towards_End-to-End_Unified_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15143 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Long_Towards_End-to-End_Unified_Scene_Text_Detection_and_Layout_Analysis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Long_Towards_End-to-End_Unified_Scene_Text_Detection_and_Layout_Analysis_CVPR_2022_paper.html | CVPR 2022 | null |
Image Based Reconstruction of Liquids From 2D Surface Detections | Florian Richter, Ryan K. Orosco, Michael C. Yip | In this work, we present a solution to the challenging problem of reconstructing liquids from image data. The challenges in reconstructing liquids, which is not faced in previous reconstruction works on rigid and deforming surfaces, lies in the inability to use depth sensing and color features due the variable index of refraction, opacity, and environmental reflections. Therefore, we limit ourselves to only surface detections (i.e. binary mask) of liquids as observations and do not assume any prior knowledge on the liquids properties. A novel optimization problem is posed which reconstructs the liquid as particles by minimizing the error between a rendered surface from the particles and the surface detections while satisfying liquid constraints. Our solvers to this optimization problem are presented and no training data is required to apply them. We also propose a dynamic prediction to seed the reconstruction optimization from the previous time-step. We test our proposed methods in simulation and on two new liquid datasets which we open source so the broader research community can continue developing in this under explored area. | https://openaccess.thecvf.com/content/CVPR2022/papers/Richter_Image_Based_Reconstruction_of_Liquids_From_2D_Surface_Detections_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Richter_Image_Based_Reconstruction_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2111.11491 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Richter_Image_Based_Reconstruction_of_Liquids_From_2D_Surface_Detections_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Richter_Image_Based_Reconstruction_of_Liquids_From_2D_Surface_Detections_CVPR_2022_paper.html | CVPR 2022 | null |
Contextual Outpainting With Object-Level Contrastive Learning | Jiacheng Li, Chang Chen, Zhiwei Xiong | We study the problem of contextual outpainting, which aims to hallucinate the missing background contents based on the remaining foreground contents. Existing image outpainting methods focus on completing object shapes or extending existing scenery textures, neglecting the semantically meaningful relationship between the missing and remaining contents. To explore the semantic cues provided by the remaining foreground contents, we propose a novel ConTextual Outpainting GAN (CTO-GAN), leveraging the semantic layout as a bridge to synthesize coherent and diverse background contents. To model the contextual correlation between foreground and background contents, we incorporate an object-level contrastive loss to regularize the learning of cross-modal representations of foreground contents and the corresponding background semantic layout, facilitating accurate semantic reasoning. Furthermore, we improve the realism of the generated background contents via detecting generated context in adversarial training. Extensive experiments demonstrate that the proposed method achieves superior performance compared with existing solutions on the challenging COCO-stuff dataset. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Contextual_Outpainting_With_Object-Level_Contrastive_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Contextual_Outpainting_With_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Contextual_Outpainting_With_Object-Level_Contrastive_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Contextual_Outpainting_With_Object-Level_Contrastive_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Network | Wooseok Lee, Sanghyun Son, Kyoung Mu Lee | Blind-spot network (BSN) and its variants have made significant advances in self-supervised denoising. Nevertheless, they are still bound to synthetic noisy inputs due to less practical assumptions like pixel-wise independent noise. Hence, it is challenging to deal with spatially correlated real-world noise using self-supervised BSN. Recently, pixel-shuffle downsampling (PD) has been proposed to remove the spatial correlation of real-world noise. However, it is not trivial to integrate PD and BSN directly, which prevents the fully self-supervised denoising model on real-world images. We propose an Asymmetric PD (AP) to address this issue, which introduces different PD stride factors for training and inference. We systematically demonstrate that the proposed AP can resolve inherent trade-offs caused by specific PD stride factors and make BSN applicable to practical scenarios. To this end, we develop AP-BSN, a state-of-the-art self-supervised denoising method for real-world sRGB images. We further propose random-replacing refinement, which significantly improves the performance of our AP-BSN without any additional parameters. Extensive studies demonstrate that our method outperforms the other self-supervised and even unpaired denoising methods by a large margin, without using any additional knowledge, e.g., noise level, regarding the underlying unknown noise. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_AP-BSN_Self-Supervised_Denoising_for_Real-World_Images_via_Asymmetric_PD_and_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lee_AP-BSN_Self-Supervised_Denoising_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_AP-BSN_Self-Supervised_Denoising_for_Real-World_Images_via_Asymmetric_PD_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_AP-BSN_Self-Supervised_Denoising_for_Real-World_Images_via_Asymmetric_PD_and_CVPR_2022_paper.html | CVPR 2022 | null |
AutoSDF: Shape Priors for 3D Completion, Reconstruction and Generation | Paritosh Mittal, Yen-Chi Cheng, Maneesh Singh, Shubham Tulsiani | Powerful priors allow us to perform inference with insufficient information. In this paper, we propose an autoregressive prior for 3D shapes to solve multimodal 3D tasks such as shape completion, reconstruction, and generation. We model the distribution over 3D shapes as a non-sequential autoregressive distribution over a discretized, low-dimensional, symbolic grid-like latent representation of 3D shapes. This enables us to represent distributions over 3D shapes conditioned on information from an arbitrary set of spatially anchored query locations and thus perform shape completion in such arbitrary settings (e.g. generating a complete chair given only a view of the back leg). We also show that the learned autoregressive prior can be leveraged for conditional tasks such as single-view reconstruction and language-based generation. This is achieved by learning task-specific 'naive' conditionals which can be approximated by light-weight models trained on minimal paired data. We validate the effectiveness of the proposed method using both quantitative and qualitative evaluation and show that the proposed method outperforms the specialized state-of-the-art methods trained for individual tasks. The project page with code and video visualizations can be found at https://yccyenchicheng.github.io/AutoSDF/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mittal_AutoSDF_Shape_Priors_for_3D_Completion_Reconstruction_and_Generation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.09516 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mittal_AutoSDF_Shape_Priors_for_3D_Completion_Reconstruction_and_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mittal_AutoSDF_Shape_Priors_for_3D_Completion_Reconstruction_and_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior | Metin Ersin Arican, Ozgur Kara, Gustav Bredell, Ender Konukoglu | Recent works show that convolutional neural network (CNN) architectures have a spectral bias towards lower frequencies, which has been leveraged for various image restoration tasks in the Deep Image Prior (DIP) framework. The benefit of the inductive bias the network imposes in the DIP framework depends on the architecture. Therefore, researchers have studied how to automate the search to determine the best-performing model. However, common neural architecture search (NAS) techniques are resource and time-intensive. Moreover, best-performing models are determined for a whole dataset of images instead of for each image independently, which would be prohibitively expensive. In this work, we first show that optimal neural architectures in the DIP framework are image-dependent. Leveraging this insight, we then propose an image-specific NAS strategy for the DIP framework that requires substantially less training than typical NAS approaches, effectively enabling image-specific NAS. We justify the proposed strategy's effectiveness by (1) demonstrating its performance on a NAS Dataset for DIP that includes 522 models from a particular search space (2) conducting extensive experiments on image denoising, inpainting, and super-resolution tasks. Our experiments show that image-specific metrics can reduce the search space to a small cohort of models, of which the best model outperforms current NAS approaches for image restoration. Codes and datasets are available at https://github.com/ozgurkara99/ISNAS-DIP. | https://openaccess.thecvf.com/content/CVPR2022/papers/Arican_ISNAS-DIP_Image-Specific_Neural_Architecture_Search_for_Deep_Image_Prior_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Arican_ISNAS-DIP_Image-Specific_Neural_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Arican_ISNAS-DIP_Image-Specific_Neural_Architecture_Search_for_Deep_Image_Prior_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Arican_ISNAS-DIP_Image-Specific_Neural_Architecture_Search_for_Deep_Image_Prior_CVPR_2022_paper.html | CVPR 2022 | null |
Depth-Guided Sparse Structure-From-Motion for Movies and TV Shows | Sheng Liu, Xiaohan Nie, Raffay Hamid | Existing approaches for Structure from Motion (SfM) produce impressive 3D reconstruction results especially when using imagery captured with large parallax. However, to create engaging video-content in movies and TV shows, the amount by which a camera can be moved while filming a particular shot is often limited. The resulting small-motion parallax between video frames makes standard geometry-based SfM approaches not as effective for movies and TV shows. To address this challenge, we propose a simple yet effective approach that uses single-frame depth-prior obtained from a pretrained network to significantly improve geometry-based SfM for our small-parallax setting. To this end, we first use the depth-estimates of the detected keypoints to reconstruct the point cloud and camera-pose for initial two-view reconstruction. We then perform depth-regularized optimization to register new images and triangulate the new points during incremental reconstruction. To comprehensively evaluate our approach, we introduce a new dataset (StudioSfM) consisting of 130 shots with 21K frames from 15 studio-produced videos that are manually annotated by a professional CG studio. We demonstrate that our approach: (a) significantly improves the quality of 3D reconstruction for our small-parallax setting, (b) does not cause any degradation for data with large-parallax, and (c) maintains the generalizability and scalability of geometry-based sparse SfM. Our dataset can be obtained at github.com/amazon-research/small-baseline-camera-tracking. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Depth-Guided_Sparse_Structure-From-Motion_for_Movies_and_TV_Shows_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Depth-Guided_Sparse_Structure-From-Motion_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2204.02509 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Depth-Guided_Sparse_Structure-From-Motion_for_Movies_and_TV_Shows_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Depth-Guided_Sparse_Structure-From-Motion_for_Movies_and_TV_Shows_CVPR_2022_paper.html | CVPR 2022 | null |
End-to-End Referring Video Object Segmentation With Multimodal Transformers | Adam Botach, Evgenii Zheltonozhskii, Chaim Baskin | The referring video object segmentation task (RVOS) involves segmentation of a text-referred object instance in the frames of a given video. Due to the complex nature of this multimodal task, which combines text reasoning, video understanding, instance segmentation and tracking, existing approaches typically rely on sophisticated pipelines in order to tackle it. In this paper, we propose a simple Transformer-based approach to RVOS. Our framework, termed Multimodal Tracking Transformer (MTTR), models the RVOS task as a sequence prediction problem. Following recent advancements in computer vision and natural language processing, MTTR is based on the realization that video and text can be processed together effectively and elegantly by a single multimodal Transformer model. MTTR is end-to-end trainable, free of text-related inductive bias components and requires no additional mask-refinement post-processing steps. As such, it simplifies the RVOS pipeline considerably compared to existing methods. Evaluation on standard benchmarks reveals that MTTR significantly outperforms previous art across multiple metrics. In particular, MTTR shows impressive +5.7 and +5.0 mAP gains on the A2D-Sentences and JHMDB-Sentences datasets respectively, while processing 76 frames per second. In addition, we report strong results on the public validation set of Refer-YouTube-VOS, a more challenging RVOS dataset that has yet to receive the attention of researchers. The code to reproduce our experiments is available at https://github.com/mttr2021/MTTR | https://openaccess.thecvf.com/content/CVPR2022/papers/Botach_End-to-End_Referring_Video_Object_Segmentation_With_Multimodal_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Botach_End-to-End_Referring_Video_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.14821 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Botach_End-to-End_Referring_Video_Object_Segmentation_With_Multimodal_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Botach_End-to-End_Referring_Video_Object_Segmentation_With_Multimodal_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
Unpaired Cartoon Image Synthesis via Gated Cycle Mapping | Yifang Men, Yuan Yao, Miaomiao Cui, Zhouhui Lian, Xuansong Xie, Xian-Sheng Hua | In this paper, we present a general-purpose solution to cartoon image synthesis with unpaired training data. In contrast to previous works learning pre-defined cartoon styles for specified usage scenarios (portrait or scene), we aim to train a common cartoon translator which can not only simultaneously render exaggerated anime faces and realistic cartoon scenes, but also provide flexible user controls for desired cartoon styles. It is challenging due to the complexity of the task and the absence of paired data. The core idea of the proposed method is to introduce gated cycle mapping, that utilizes a novel gated mapping unit to produce the category-specific style code and embeds this code into cycle networks to control the translation process. For the concept of category, we classify images into different categories (e.g., 4 types: photo/cartoon portrait/scene) and learn finer-grained category translations rather than overall mappings between two domains (e.g., photo and cartoon). Furthermore, the proposed method can be easily extended to cartoon video generation with an auxiliary dataset and a new adaptive style loss. Experimental results demonstrate the superiority of the proposed method over the state of the art and validate its effectiveness in the brand-new task of general cartoon image synthesis. | https://openaccess.thecvf.com/content/CVPR2022/papers/Men_Unpaired_Cartoon_Image_Synthesis_via_Gated_Cycle_Mapping_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Men_Unpaired_Cartoon_Image_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Men_Unpaired_Cartoon_Image_Synthesis_via_Gated_Cycle_Mapping_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Men_Unpaired_Cartoon_Image_Synthesis_via_Gated_Cycle_Mapping_CVPR_2022_paper.html | CVPR 2022 | null |
IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo | Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys | We present IterMVS, a new data-driven method for high-resolution multi-view stereo. We propose a novel GRU-based estimator that encodes pixel-wise probability distributions of depth in its hidden state. Ingesting multi-scale matching information, our model refines these distributions over multiple iterations and infers depth and confidence. To extract the depth maps, we combine traditional classification and regression in a novel manner. We verify the efficiency and effectiveness of our method on DTU, Tanks&Temples and ETH3D. While being the most efficient method in both memory and run-time, our model achieves competitive performance on DTU and better generalization ability on Tanks&Temples as well as ETH3D than most state-of-the-art methods. Code is available at https://github.com/FangjinhuaWang/IterMVS. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_IterMVS_Iterative_Probability_Estimation_for_Efficient_Multi-View_Stereo_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_IterMVS_Iterative_Probability_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.05126 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_IterMVS_Iterative_Probability_Estimation_for_Efficient_Multi-View_Stereo_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_IterMVS_Iterative_Probability_Estimation_for_Efficient_Multi-View_Stereo_CVPR_2022_paper.html | CVPR 2022 | null |
Not All Points Are Equal: Learning Highly Efficient Point-Based Detectors for 3D LiDAR Point Clouds | Yifan Zhang, Qingyong Hu, Guoquan Xu, Yanxin Ma, Jianwei Wan, Yulan Guo | We study the problem of efficient object detection of 3D LiDAR point clouds. To reduce the memory and computational cost, existing point-based pipelines usually adopt task-agnostic random sampling or farthest point sampling to progressively downsample input point clouds, despite the fact that not all points are equally important to the task of object detection. In particular, the foreground points are inherently more important than background points for object detectors. Motivated by this, we propose a highly-efficient single-stage point-based 3D detector in this paper, termed IA-SSD. The key of our approach is to exploit two learnable, task-oriented, instance-aware downsampling strategies to hierarchically select the foreground points belonging to objects of interest. Additionally, we also introduce a contextual centroid perception module to further estimate precise instance centers. Finally, we build our \nickname following the encoder-only architecture for efficiency. Extensive experiments conducted on several large-scale detection benchmarks demonstrate the competitive performance of our IA-SSD. Thanks to the low memory footprint and a high degree of parallelism, it achieves a superior speed of 80+ frames-per-second on the KITTI dataset with a single RTX2080Ti GPU. The code is available at https://github.com/yifanzhang713/IA-SSD. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Not_All_Points_Are_Equal_Learning_Highly_Efficient_Point-Based_Detectors_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Not_All_Points_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.11139 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Not_All_Points_Are_Equal_Learning_Highly_Efficient_Point-Based_Detectors_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Not_All_Points_Are_Equal_Learning_Highly_Efficient_Point-Based_Detectors_CVPR_2022_paper.html | CVPR 2022 | null |
FedCorr: Multi-Stage Federated Learning for Label Noise Correction | Jingyi Xu, Zihan Chen, Tony Q.S. Quek, Kai Fong Ernest Chong | Federated learning (FL) is a privacy-preserving distributed learning paradigm that enables clients to jointly train a global model. In real-world FL implementations, client data could have label noise, and different clients could have vastly different label noise levels. Although there exist methods in centralized learning for tackling label noise, such methods do not perform well on heterogeneous label noise in FL settings, due to the typically smaller sizes of client datasets and data privacy requirements in FL. In this paper, we propose FedCorr, a general multi-stage framework to tackle heterogeneous label noise in FL, without making any assumptions on the noise models of local clients, while still maintaining client data privacy. In particular, (1) FedCorr dynamically identifies noisy clients by exploiting the dimensionalities of the model prediction subspaces independently measured on all clients, and then identifies incorrect labels on noisy clients based on per-sample losses. To deal with data heterogeneity and to increase training stability, we propose an adaptive local proximal regularization term that is based on estimated local noise levels. (2) We further finetune the global model on identified clean clients and correct the noisy labels for the remaining noisy clients after finetuning. (3) Finally, we apply the usual training on all clients to make full use of all local data. Experiments conducted on CIFAR-10/100 with federated synthetic label noise, and on a real-world noisy dataset, Clothing1M, demonstrate that FedCorr is robust to label noise and substantially outperforms the state-of-the-art methods at multiple noise levels. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_FedCorr_Multi-Stage_Federated_Learning_for_Label_Noise_Correction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_FedCorr_Multi-Stage_Federated_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.04677 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_FedCorr_Multi-Stage_Federated_Learning_for_Label_Noise_Correction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_FedCorr_Multi-Stage_Federated_Learning_for_Label_Noise_Correction_CVPR_2022_paper.html | CVPR 2022 | null |
Detecting Camouflaged Object in Frequency Domain | Yijie Zhong, Bo Li, Lv Tang, Senyun Kuang, Shuang Wu, Shouhong Ding | Camouflaged object detection (COD) aims to identify objects that are perfectly embedded in their environment, which has various downstream applications in fields such as medicine, art, and agriculture. However, it is an extremely challenging task to spot camouflaged objects with the perception ability of human eyes. Hence, we claim that the goal of COD task is not just to mimic the human visual ability in a single RGB domain, but to go beyond the human biological vision. We then introduce the frequency domain as an additional clue to better detect camouflaged objects from backgrounds. To well involve the frequency clues into the CNN models, we present a powerful network with two special components. We first design a novel frequency enhancement module (FEM) to dig clues of camouflaged objects in the frequency domain. It contains the offline discrete cosine transform followed by the learnable enhancement. Then we use a feature alignment to fuse the features from RGB domain and frequency domain. Moreover, to further make full use of the frequency information, we propose the high-order relation module (HOR) to handle the rich fusion feature. Comprehensive experiments on three widely-used COD datasets show the proposed method significantly outperforms other state-of-the-art methods by a large margin. The code and results are released in https://github.com/luckybird1994/FDCOD. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhong_Detecting_Camouflaged_Object_in_Frequency_Domain_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhong_Detecting_Camouflaged_Object_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhong_Detecting_Camouflaged_Object_in_Frequency_Domain_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhong_Detecting_Camouflaged_Object_in_Frequency_Domain_CVPR_2022_paper.html | CVPR 2022 | null |
RigNeRF: Fully Controllable Neural 3D Portraits | ShahRukh Athar, Zexiang Xu, Kalyan Sunkavalli, Eli Shechtman, Zhixin Shu | Volumetric neural rendering methods, such as neural ra-diance fields (NeRFs), have enabled photo-realistic novel view synthesis. However, in their standard form, NeRFs do not support the editing of objects, such as a human head,within a scene. In this work, we propose RigNeRF, a system that goes beyond just novel view synthesis and enables full control of head pose and facial expressions learned from a single portrait video. We model changes in head pose and facial expressions using a deformation field that is guided by a 3D morphable face model (3DMM). The 3DMM effectively acts as a prior for RigNeRF that learns to predict only residuals to the 3DMM deformations and allows us to render novel (rigid) poses and (non-rigid) expressions that were not present in the input sequence. Using only a smartphone-captured short video of a subject for training,we demonstrate the effectiveness of our method on free view synthesis of a portrait scene with explicit head pose and expression controls. | https://openaccess.thecvf.com/content/CVPR2022/papers/Athar_RigNeRF_Fully_Controllable_Neural_3D_Portraits_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Athar_RigNeRF_Fully_Controllable_Neural_3D_Portraits_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Athar_RigNeRF_Fully_Controllable_Neural_3D_Portraits_CVPR_2022_paper.html | CVPR 2022 | null |
CLIP-Forge: Towards Zero-Shot Text-To-Shape Generation | Aditya Sanghi, Hang Chu, Joseph G. Lambourne, Ye Wang, Chin-Yi Cheng, Marco Fumero, Kamal Rahimi Malekshan | Generating shapes using natural language can enable new ways of imagining and creating the things around us. While significant recent progress has been made in text-to-image generation, text-to-shape generation remains a challenging problem due to the unavailability of paired text and shape data at a large scale. We present a simple yet effective method for zero-shot text-to-shape generation that circumvents such data scarcity. Our proposed method, named CLIP-Forge, is based on a two-stage training process, which only depends on an unlabelled shape dataset and a pre-trained image-text network such as CLIP. Our method has the benefits of avoiding expensive inference time optimization, as well as the ability to generate multiple shapes for a given text. We not only demonstrate promising zero-shot generalization of the CLIP-Forge model qualitatively and quantitatively, but also provide extensive comparative evaluations to better understand its behavior. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sanghi_CLIP-Forge_Towards_Zero-Shot_Text-To-Shape_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sanghi_CLIP-Forge_Towards_Zero-Shot_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sanghi_CLIP-Forge_Towards_Zero-Shot_Text-To-Shape_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sanghi_CLIP-Forge_Towards_Zero-Shot_Text-To-Shape_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
Style-Based Global Appearance Flow for Virtual Try-On | Sen He, Yi-Zhe Song, Tao Xiang | Image-based virtual try-on aims to fit an in-shop garment into a clothed person image. To achieve this, a key step is garment warping which spatially aligns the target garment with the corresponding body parts in the person image. Prior methods typically adopt a local appearance flow estimation model. They are thus intrinsically susceptible to difficult body poses/occlusions and large mis-alignments between person and garment images. To overcome this limitation, a novel global appearance flow estimation model is proposed in this work. For the first time, a StyleGAN based architecture is adopted for appearance flow estimation. This enables us to take advantage of a global style vector to encode a whole-image context to cope with the aforementioned challenges. To guide the StyleGAN flow generator to pay more attention to local garment deformation, a flow refinement module is introduced to add local context. Experiment results on a popular virtual try-on benchmark show that our method achieves new state-of-the-art performance. It is particularly effective in a 'in-the-wild' application scenario where the reference image is full-body resulting in a large mis-alignment with the garment image. | https://openaccess.thecvf.com/content/CVPR2022/papers/He_Style-Based_Global_Appearance_Flow_for_Virtual_Try-On_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.01046 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/He_Style-Based_Global_Appearance_Flow_for_Virtual_Try-On_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/He_Style-Based_Global_Appearance_Flow_for_Virtual_Try-On_CVPR_2022_paper.html | CVPR 2022 | null |
Source-Free Object Detection by Learning To Overlook Domain Style | Shuaifeng Li, Mao Ye, Xiatian Zhu, Lihua Zhou, Lin Xiong | Source-free object detection (SFOD) needs to adapt a detector pre-trained on a labeled source domain to a target domain, with only unlabeled training data from the target domain. Existing SFOD methods typically adopt the pseudo labeling paradigm with model adaption alternating between predicting pseudo labels and fine-tuning the model. This approach suffers from both unsatisfactory accuracy of pseudo labels due to the presence of domain shift and limited use of target domain training data. In this work, we present a novel Learning to Overlook Domain Style (LODS) method with such limitations solved in a principled manner. Our idea is to reduce the domain shift effect by enforcing the model to overlook the target domain style, such that model adaptation is simplified and becomes easier to carry on. To that end, we enhance the style of each target domain image and leverage the style degree difference between the original image and the enhanced image as a self-supervised signal for model adaptation. By treating the enhanced image as an auxiliary view, we exploit a student-teacher architecture for learning to overlook the style degree difference against the original image, also characterized with a novel style enhancement algorithm and graph alignment constraint. Extensive experiments demonstrate that our LODS yields new state-of-the-art performance on four benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Source-Free_Object_Detection_by_Learning_To_Overlook_Domain_Style_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Source-Free_Object_Detection_by_Learning_To_Overlook_Domain_Style_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Source-Free_Object_Detection_by_Learning_To_Overlook_Domain_Style_CVPR_2022_paper.html | CVPR 2022 | null |
Active Learning for Open-Set Annotation | Kun-Peng Ning, Xun Zhao, Yu Li, Sheng-Jun Huang | Existing active learning studies typically work in the closed-set setting by assuming that all data examples to be labeled are drawn from known classes. However, in real annotation tasks, the unlabeled data usually contains a large amount of examples from unknown classes, resulting in the failure of most active learning methods. To tackle this open-set annotation (OSA) problem, we propose a new active learning framework called LfOSA, which boosts the classification performance with an effective sampling strategy to precisely detect examples from known classes for annotation. The LfOSA framework introduces an auxiliary network to model the per-example max activation value (MAV) distribution with a Gaussian Mixture Model, which can dynamically select the examples with highest probability from known classes in the unlabeled set. Moreover, by reducing the temperature T of the loss function, the detection model will be further optimized by exploiting both known and unknown supervision. The experimental results show that the proposed method can significantly improve the selection quality of known classes, and achieve higher classification accuracy with lower annotation cost than state-of-the-art active learning methods. To the best of our knowledge, this is the first work of active learning for open-set annotation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ning_Active_Learning_for_Open-Set_Annotation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2201.06758 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ning_Active_Learning_for_Open-Set_Annotation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ning_Active_Learning_for_Open-Set_Annotation_CVPR_2022_paper.html | CVPR 2022 | null |
SceneSqueezer: Learning To Compress Scene for Camera Relocalization | Luwei Yang, Rakesh Shrestha, Wenbo Li, Shuaicheng Liu, Guofeng Zhang, Zhaopeng Cui, Ping Tan | Standard visual localization methods build a priori 3D model of a scene which is used to establish correspondences against the 2D keypoints in a query image. Storing these pre-built 3D scene models can be prohibitively expensive for large-scale environments, especially on mobile devices with limited storage and communication bandwidth. We design a novel framework that compresses a scene while still maintaining localization accuracy. The scene is compressed in three stages: first, the database frames are clustered using pairwise co-visibility information. Then, a learned point selection module prunes the points in each cluster taking into account the final pose estimation accuracy. In the final stage, the features of the selected points are further compressed using learned quantization. Query image registration is done using only the compressed scene points. To the best of our knowledge, we are the first to propose learned scene compression for visual localization. We also demonstrate the effectiveness and efficiency of our method on various outdoor datasets where it can perform accurate localization with low memory consumption. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_SceneSqueezer_Learning_To_Compress_Scene_for_Camera_Relocalization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_SceneSqueezer_Learning_To_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_SceneSqueezer_Learning_To_Compress_Scene_for_Camera_Relocalization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_SceneSqueezer_Learning_To_Compress_Scene_for_Camera_Relocalization_CVPR_2022_paper.html | CVPR 2022 | null |
SelfRecon: Self Reconstruction Your Digital Avatar From Monocular Video | Boyi Jiang, Yang Hong, Hujun Bao, Juyong Zhang | We propose SelfRecon, a clothed human body reconstruction method that combines implicit and explicit representations to recover space-time coherent geometries from a monocular self-rotating human video. Explicit methods require a predefined template mesh for a given sequence, while the template is hard to acquire for a specific subject. Meanwhile, the fixed topology limits the reconstruction accuracy and clothing types. Implicit representation supports arbitrary topology and can represent high-fidelity geometry shapes due to its continuous nature. However, it is difficult to integrate multi-frame information to produce a consistent registration sequence for downstream applications. We propose to combine the advantages of both representations. We utilize differential mask loss of the explicit mesh to obtain the coherent overall shape, while the details on the implicit surface are refined with the differentiable neural rendering. Meanwhile, the explicit mesh is updated periodically to adjust its topology changes, and a consistency loss is designed to match both representations. Compared with existing methods, SelfRecon can produce high-fidelity surfaces for arbitrary clothed humans with self-supervised optimization. Extensive experimental results demonstrate its effectiveness on real captured monocular videos. The source code is available at https://github.com/jby1993/SelfReconCode. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jiang_SelfRecon_Self_Reconstruction_Your_Digital_Avatar_From_Monocular_Video_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2201.12792 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_SelfRecon_Self_Reconstruction_Your_Digital_Avatar_From_Monocular_Video_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_SelfRecon_Self_Reconstruction_Your_Digital_Avatar_From_Monocular_Video_CVPR_2022_paper.html | CVPR 2022 | null |
Instance-Dependent Label-Noise Learning With Manifold-Regularized Transition Matrix Estimation | De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama | In label-noise learning, estimating the transition matrix has attracted more and more attention as the matrix plays an important role in building statistically consistent classifiers. However, it is very challenging to estimate the transition matrix T(x), where T(x) denotes the instance, because it is unidentifiable under the instance-dependent noise (IDN). To address this problem, we have noticed that, there are psychological and physiological evidences showing that we humans are more likely to annotate instances of similar appearances to the same classes, and thus poor-quality or ambiguous instances of similar appearances are easier to be mislabeled to the correlated or same noisy classes. Therefore, we propose assumption on the geometry of T(x) that "the closer two instances are, the more similar their corresponding transition matrices should be". More specifically, we formulate above assumption into the manifold embedding, to effectively reduce the degree of freedom of T(x) and make it stably estimable in practice. This proposed manifold-regularized technique works by directly reducing the estimation error without hurting the approximation error about the estimation problem of T(x) Experimental evaluations on four synthetic and two real-world datasets demonstrate our method is superior to state-of-the-art approaches for label-noise learning under the challenging IDN. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cheng_Instance-Dependent_Label-Noise_Learning_With_Manifold-Regularized_Transition_Matrix_Estimation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_Instance-Dependent_Label-Noise_Learning_With_Manifold-Regularized_Transition_Matrix_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_Instance-Dependent_Label-Noise_Learning_With_Manifold-Regularized_Transition_Matrix_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
Rethinking the Augmentation Module in Contrastive Learning: Learning Hierarchical Augmentation Invariance With Expanded Views | Junbo Zhang, Kaisheng Ma | A data augmentation module is utilized in contrastive learning to transform the given data example into two views, which is considered essential and irreplaceable. However, the pre-determined composition of multiple data augmentations brings two drawbacks. First, the artificial choice of augmentation types brings specific representational invariances to the model, which have different degrees of positive and negative effects on different downstream tasks. Treating each type of augmentation equally during training makes the model learn non-optimal representations for various downstream tasks and limits the flexibility to choose augmentation types beforehand. Second, the strong data augmentations used in classic contrastive learning methods may bring too much invariance in some cases, and fine-grained information that is essential to some downstream tasks may be lost. This paper proposes a general method to alleviate these two problems by considering "where" and "what" to contrast in a general contrastive learning framework. We first propose to learn different augmentation invariances at different depths of the model according to the importance of each data augmentation instead of learning representational invariances evenly in the backbone. We then propose to expand the contrast content with augmentation embeddings to reduce the misleading effects of strong data augmentations. Experiments based on several baseline methods demonstrate that we learn better representations for various benchmarks on classification, detection, and segmentation downstream tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Rethinking_the_Augmentation_Module_in_Contrastive_Learning_Learning_Hierarchical_Augmentation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2206.00227 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Rethinking_the_Augmentation_Module_in_Contrastive_Learning_Learning_Hierarchical_Augmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Rethinking_the_Augmentation_Module_in_Contrastive_Learning_Learning_Hierarchical_Augmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Supervised Models Are Continual Learners | Enrico Fini, Victor G. Turrisi da Costa, Xavier Alameda-Pineda, Elisa Ricci, Karteek Alahari, Julien Mairal | Self-supervised models have been shown to produce comparable or better visual representations than their supervised counterparts when trained offline on unlabeled data at scale. However, their efficacy is catastrophically reduced in a Continual Learning (CL) scenario where data is presented to the model sequentially. In this paper, we show that self-supervised loss functions can be seamlessly converted into distillation mechanisms for CL by adding a predictor network that maps the current state of the representations to their past state. This enables us to devise a framework for Continual self-supervised visual representation Learning that (i) significantly improves the quality of the learned representations, (ii) is compatible with several state-of-the-art self-supervised objectives, and (iii) needs little to no hyperparameter tuning. We demonstrate the effectiveness of our approach empirically by training six popular self-supervised models in various CL settings. Code: github.com/DonkeyShot21/cassle | https://openaccess.thecvf.com/content/CVPR2022/papers/Fini_Self-Supervised_Models_Are_Continual_Learners_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fini_Self-Supervised_Models_Are_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fini_Self-Supervised_Models_Are_Continual_Learners_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fini_Self-Supervised_Models_Are_Continual_Learners_CVPR_2022_paper.html | CVPR 2022 | null |
Dreaming To Prune Image Deraining Networks | Weiqi Zou, Yang Wang, Xueyang Fu, Yang Cao | Convolutional image deraining networks have achieved great success while suffering from tremendous computational and memory costs. Most model compression methods require original data for iterative fine-tuning, which is limited in real-world applications due to storage, privacy, and transmission constraints. We note that it is overstretched to fine-tune the compressed model using self-collected data, as it exhibits poor generalization over images with different degradation characteristics. To address this problem, we propose a novel data-free compression framework for deraining networks. It is based on our observation that deep degradation representations can be clustered by degradation characteristics (types of rain) while independent of image content. Therefore, in our framework, we "dream" diverse in-distribution degraded images using a deep inversion paradigm, thus leveraging them to distill the pruned model. Specifically, we preserve the performance of the pruned model in a dual-branch way. In one branch, we invert the pre-trained model (teacher) to reconstruct the degraded inputs that resemble the original distribution and employ the orthogonal regularization for deep features to yield degradation diversity. In the other branch, the pruned model (student) is distilled to fit the teacher's original statistical modeling on these dreamed inputs. Further, an adaptive pruning scheme is proposed to determine the hierarchical sparsity, which alleviates the regression drift of the initial pruned model. Experiments on various deraining datasets demonstrate that our method can reduce about 40% FLOPs of the state-of-the-art models while maintaining comparable performance without original data. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zou_Dreaming_To_Prune_Image_Deraining_Networks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zou_Dreaming_To_Prune_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zou_Dreaming_To_Prune_Image_Deraining_Networks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zou_Dreaming_To_Prune_Image_Deraining_Networks_CVPR_2022_paper.html | CVPR 2022 | null |
Equivariant Point Cloud Analysis via Learning Orientations for Message Passing | Shitong Luo, Jiahan Li, Jiaqi Guan, Yufeng Su, Chaoran Cheng, Jian Peng, Jianzhu Ma | Equivariance has been a long-standing concern in various fields ranging from computer vision to physical modeling. Most previous methods struggle with generality, simplicity, and expressiveness --- some are designed ad hoc for specific data types, some are too complex to be accessible, and some sacrifice flexible transformations. In this work, we propose a novel and simple framework to achieve equivariance for point cloud analysis based on the message passing (graph neural network) scheme. We find the equivariant property could be obtained by introducing an orientation for each point to decouple the relative position for each point from the global pose of the entire point cloud. Therefore, we extend current message passing networks with a module that learns orientations for each point. Before aggregating information from the neighbors of a point, the networks transforms the neighbors' coordinates based on the point's learned orientations. We provide formal proofs to show the equivariance of the proposed framework. Empirically, we demonstrate that our proposed method is competitive on both point cloud analysis and physical modeling tasks. Code is available at https://github.com/luost26/Equivariant-OrientedMP. | https://openaccess.thecvf.com/content/CVPR2022/papers/Luo_Equivariant_Point_Cloud_Analysis_via_Learning_Orientations_for_Message_Passing_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Luo_Equivariant_Point_Cloud_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14486 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Luo_Equivariant_Point_Cloud_Analysis_via_Learning_Orientations_for_Message_Passing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Luo_Equivariant_Point_Cloud_Analysis_via_Learning_Orientations_for_Message_Passing_CVPR_2022_paper.html | CVPR 2022 | null |
When Does Contrastive Visual Representation Learning Work? | Elijah Cole, Xuan Yang, Kimberly Wilber, Oisin Mac Aodha, Serge Belongie | Recent self-supervised representation learning techniques have largely closed the gap between supervised and unsupervised learning on ImageNet classification. While the particulars of pretraining on ImageNet are now relatively well understood, the field still lacks widely accepted best practices for replicating this success on other datasets. As a first step in this direction, we study contrastive self-supervised learning on four diverse large-scale datasets. By looking through the lenses of data quantity, data domain, data quality, and task granularity, we provide new insights into the necessary conditions for successful self-supervised learning. Our key findings include observations such as: (i) the benefit of additional pretraining data beyond 500k images is modest, (ii) adding pretraining images from another domain does not lead to more general representations, (iii) corrupted pretraining images have a disparate impact on supervised and self-supervised pretraining, and (iv) contrastive learning lags far behind supervised learning on fine-grained visual classification tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cole_When_Does_Contrastive_Visual_Representation_Learning_Work_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2105.05837 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cole_When_Does_Contrastive_Visual_Representation_Learning_Work_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cole_When_Does_Contrastive_Visual_Representation_Learning_Work_CVPR_2022_paper.html | CVPR 2022 | null |
One Step at a Time: Long-Horizon Vision-and-Language Navigation With Milestones | Chan Hee Song, Jihyung Kil, Tai-Yu Pan, Brian M. Sadler, Wei-Lun Chao, Yu Su | We study the problem of developing autonomous agents that can follow human instructions to infer and perform a sequence of actions to complete the underlying task. Significant progress has been made in recent years, especially for tasks with short horizons. However, when it comes to long-horizon tasks with extended sequences of actions, an agent can easily ignore some instructions or get stuck in the middle of the long instructions and eventually fail the task. To address this challenge, we propose a model-agnostic milestone-based task tracker (M-Track) to guide the agent and monitor its progress. Specifically, we propose a milestone builder that tags the instructions with navigation and interaction milestones which the agent needs to complete step by step, and a milestone checker that systemically checks the agent's progress in its current milestone and determines when to proceed to the next. On the challenging ALFRED dataset, our M-Track leads to a notable 33% and 52% relative improvement in unseen success rate over two competitive base models. | https://openaccess.thecvf.com/content/CVPR2022/papers/Song_One_Step_at_a_Time_Long-Horizon_Vision-and-Language_Navigation_With_Milestones_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Song_One_Step_at_a_Time_Long-Horizon_Vision-and-Language_Navigation_With_Milestones_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.07028 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Song_One_Step_at_a_Time_Long-Horizon_Vision-and-Language_Navigation_With_Milestones_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Song_One_Step_at_a_Time_Long-Horizon_Vision-and-Language_Navigation_With_Milestones_CVPR_2022_paper.html | CVPR 2022 | null |
Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization | Wei Dong, Junsheng Wu, Yi Luo, Zongyuan Ge, Peng Wang | The key towards learning informative node representations in graphs lies in how to gain contextual information from the neighbourhood. In this work, we present a simple-yet-effective self-supervised node representation learning strategy via directly maximizing the mutual information between the hidden representations of nodes and their neighbourhood, which can be theoretically justified by its link to graph smoothing. Following InfoNCE, our framework is optimized via a surrogate contrastive loss, where the positive selection underpins the quality and efficiency of representation learning. To this end, we propose a topology-aware positive sampling strategy, which samples positives from the neighbourhood by considering the structural dependencies between nodes and thus enables positive selection upfront. In the extreme case when only one positive is sampled, we fully avoid expensive neighbourhood aggregation. Our methods achieve promising performance on various node classification datasets. It is also worth mentioning by applying our loss function to MLP based node encoders, our methods can be orders of faster than existing solutions. Our codes and supplementary materials are available at https://github.com/dongwei156/n2n. | https://openaccess.thecvf.com/content/CVPR2022/papers/Dong_Node_Representation_Learning_in_Graph_via_Node-to-Neighbourhood_Mutual_Information_Maximization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dong_Node_Representation_Learning_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.12265 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Node_Representation_Learning_in_Graph_via_Node-to-Neighbourhood_Mutual_Information_Maximization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Node_Representation_Learning_in_Graph_via_Node-to-Neighbourhood_Mutual_Information_Maximization_CVPR_2022_paper.html | CVPR 2022 | null |
Point Cloud Pre-Training With Natural 3D Structures | Ryosuke Yamada, Hirokatsu Kataoka, Naoya Chiba, Yukiyasu Domae, Tetsuya Ogata | The construction of 3D point cloud datasets requires a great deal of human effort. Therefore, constructing a largescale 3D point clouds dataset is difficult. In order to remedy this issue, we propose a newly developed point cloud fractal database (PC-FractalDB), which is a novel family of formula-driven supervised learning inspired by fractal geometry encountered in natural 3D structures. Our research is based on the hypothesis that we could learn representations from more real-world 3D patterns than conventional 3D datasets by learning fractal geometry. We show how the PC-FractalDB facilitates solving several recent dataset-related problems in 3D scene understanding, such as 3D model collection and labor-intensive annotation. The experimental section shows how we achieved the performance rate of up to 61.9% and 59.0% for the ScanNetV2 and SUN RGB-D datasets, respectively, over the current highest scores obtained with the PointContrast, contrastive scene contexts (CSC), and RandomRooms. Moreover, the PC-FractalDB pre-trained model is especially effective in training with limited data. For example, in 10% of training data on ScanNetV2, the PC-FractalDB pre-trained VoteNet performs at 38.3%, which is +14.8% higher accuracy than CSC. Of particular note, we found that the proposed method achieves the highest results for 3D object detection pre-training in limited point cloud data. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yamada_Point_Cloud_Pre-Training_With_Natural_3D_Structures_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yamada_Point_Cloud_Pre-Training_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yamada_Point_Cloud_Pre-Training_With_Natural_3D_Structures_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yamada_Point_Cloud_Pre-Training_With_Natural_3D_Structures_CVPR_2022_paper.html | CVPR 2022 | null |
Scene Consistency Representation Learning for Video Scene Segmentation | Haoqian Wu, Keyu Chen, Yanan Luo, Ruizhi Qiao, Bo Ren, Haozhe Liu, Weicheng Xie, Linlin Shen | A long-term video, such as a movie or TV show, is composed of various scenes, each of which represents a series of shots sharing the same semantic story. Spotting the correct scene boundary from the long-term video is a challenging task, since a model must understand the storyline of the video to figure out where a scene starts and ends. To this end, we propose an effective Self-Supervised Learning (SSL) framework to learn better shot representations from unlabeled long-term videos. More specifically, we present an SSL scheme to achieve scene consistency, while exploring considerable data augmentation and shuffling methods to boost the model generalizability. Instead of explicitly learning the scene boundary features as in the previous methods, we introduce a vanilla temporal model with less inductive bias to verify the quality of the shot features. Our method achieves the state-of-the-art performance on the task of Video Scene Segmentation. Additionally, we suggest a more fair and reasonable benchmark to evaluate the performance of Video Scene Segmentation methods. The code is made available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Scene_Consistency_Representation_Learning_for_Video_Scene_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_Scene_Consistency_Representation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.05487 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Scene_Consistency_Representation_Learning_for_Video_Scene_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Scene_Consistency_Representation_Learning_for_Video_Scene_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart | Tianyu Pang, Huishuai Zhang, Di He, Yinpeng Dong, Hang Su, Wei Chen, Jun Zhu, Tie-Yan Liu | Correctly classifying adversarial examples is an essential but challenging requirement for safely deploying machine learning models. As reported in RobustBench, even the state-of-the-art adversarially trained models struggle to exceed 67% robust test accuracy on CIFAR-10, which is far from practical. A complementary way towards robustness is to introduce a rejection option, allowing the model to not return predictions on uncertain inputs, where confidence is a commonly used certainty proxy. Along with this routine, we find that confidence and a rectified confidence (R-Con) can form two coupled rejection metrics, which could provably distinguish wrongly classified inputs from correctly classified ones. This intriguing property sheds light on using coupling strategies to better detect and reject adversarial examples. We evaluate our rectified rejection (RR) module on CIFAR-10, CIFAR-10-C, and CIFAR-100 under several attacks including adaptive ones, and demonstrate that the RR module is compatible with different adversarial training frameworks on improving robustness, with little extra computation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Pang_Two_Coupled_Rejection_Metrics_Can_Tell_Adversarial_Examples_Apart_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Pang_Two_Coupled_Rejection_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2105.14785 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Pang_Two_Coupled_Rejection_Metrics_Can_Tell_Adversarial_Examples_Apart_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Pang_Two_Coupled_Rejection_Metrics_Can_Tell_Adversarial_Examples_Apart_CVPR_2022_paper.html | CVPR 2022 | null |
Exploiting Explainable Metrics for Augmented SGD | Mahdi S. Hosseini, Mathieu Tuli, Konstantinos N. Plataniotis | Explaining the generalization characteristics of deep learning is an emerging topic in advanced machine learning. There are several unanswered questions about how learning under stochastic optimization really works and why certain strategies are better than others. In this paper, we address the following question: can we probe intermediate layers of a deep neural network to identify and quantify the learning quality of each layer? With this question in mind, we propose new explainability metrics that measure the redundant information in a network's layers using a low-rank factorization framework and quantify a complexity measure that is highly correlated with the generalization performance of a given optimizer, network, and dataset. We subsequently exploit these metrics to augment the Stochastic Gradient Descent (SGD) optimizer by adaptively adjusting the learning rate in each layer to improve in generalization performance. Our augmented SGD -- dubbed RMSGD -- introduces minimal computational overhead compared to SOTA methods and outperforms them by exhibiting strong generalization characteristics across application, architecture, and dataset. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hosseini_Exploiting_Explainable_Metrics_for_Augmented_SGD_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hosseini_Exploiting_Explainable_Metrics_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16723 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hosseini_Exploiting_Explainable_Metrics_for_Augmented_SGD_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hosseini_Exploiting_Explainable_Metrics_for_Augmented_SGD_CVPR_2022_paper.html | CVPR 2022 | null |
Semi-Supervised Video Semantic Segmentation With Inter-Frame Feature Reconstruction | Jiafan Zhuang, Zilei Wang, Yuan Gao | One major challenge for semantic segmentation in real-world scenarios is only limited pixel-level labels available due to high expense of human labor though a vast volume of video data is provided. Existing semi-supervised methods attempt to exploit unlabeled data in model training, but they just regard video as a set of independent images. To better explore semi-supervised segmentation problem with video data, we formulate a semi-supervised video semantic segmentation task in this paper. For this task, we observe that the overfitting is surprisingly severe between labeled and unlabeled frames within a training video although they are very similar in style and contents. This is called inner-video overfitting, and it would actually lead to inferior performance. To tackle this issue, we propose a novel inter-frame feature reconstruction (IFR) technique to leverage the ground-truth labels to supervise the model training on unlabeled frames. IFR is essentially to utilize the internal relevance of different frames within a video. During training, IFR would enforce the feature distributions between labeled and unlabeled frames to be narrowed. Consequently, the inner-video overfitting issue can be effectively alleviated. We conduct extensive experiments on Cityscapes and CamVid, and the results demonstrate the superiority of our proposed method to previous state-of-the-art methods. The code is available at https://github.com/jfzhuang/IFR. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhuang_Semi-Supervised_Video_Semantic_Segmentation_With_Inter-Frame_Feature_Reconstruction_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhuang_Semi-Supervised_Video_Semantic_Segmentation_With_Inter-Frame_Feature_Reconstruction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhuang_Semi-Supervised_Video_Semantic_Segmentation_With_Inter-Frame_Feature_Reconstruction_CVPR_2022_paper.html | CVPR 2022 | null |
GenDR: A Generalized Differentiable Renderer | Felix Petersen, Bastian Goldluecke, Christian Borgelt, Oliver Deussen | In this work, we present and study a generalized family of differentiable renderers. We discuss from scratch which components are necessary for differentiable rendering and formalize the requirements for each component.We instantiate our general differentiable renderer, which generalizes existing differentiable renderers like SoftRas and DIB-R, with an array of different smoothing distributions to cover a large spectrum of reasonable settings. We evaluate an array of differentiable renderer instantiations on the popular ShapeNet 3D reconstruction benchmark and analyze the implications of our results. Surprisingly, the simple uniform distribution yields the best overall results when averaged over 13 classes; in general, however, the optimal choice of distribution heavily depends on the task. | https://openaccess.thecvf.com/content/CVPR2022/papers/Petersen_GenDR_A_Generalized_Differentiable_Renderer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Petersen_GenDR_A_Generalized_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.13845 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Petersen_GenDR_A_Generalized_Differentiable_Renderer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Petersen_GenDR_A_Generalized_Differentiable_Renderer_CVPR_2022_paper.html | CVPR 2022 | null |
Improving Neural Implicit Surfaces Geometry With Patch Warping | François Darmon, Bénédicte Bascle, Jean-Clément Devaux, Pascal Monasse, Mathieu Aubry | Neural implicit surfaces have become an important technique for multi-view 3D reconstruction but their accuracy remains limited. In this paper, we argue that this comes from the difficulty to learn and render high frequency textures with neural networks. We thus propose to add to the standard neural rendering optimization a direct photo-consistency term across the different views. Intuitively, we optimize the implicit geometry so that it warps views on each other in a consistent way. We demonstrate that two elements are key to the success of such an approach: (i) warping entire patches, using the predicted occupancy and normals of the 3D points along each ray, and measuring their similarity with a robust structural similarity (SSIM); (ii) handling visibility and occlusion in such a way that incorrect warps are not given too much importance while encouraging a reconstruction as complete as possible. We evaluate our approach, dubbed NeuralWarp, on the standard DTU and EPFL benchmarks and show it outperforms state of the art unsupervised implicit surfaces reconstructions by over 20% on both datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Darmon_Improving_Neural_Implicit_Surfaces_Geometry_With_Patch_Warping_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Darmon_Improving_Neural_Implicit_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.09648 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Darmon_Improving_Neural_Implicit_Surfaces_Geometry_With_Patch_Warping_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Darmon_Improving_Neural_Implicit_Surfaces_Geometry_With_Patch_Warping_CVPR_2022_paper.html | CVPR 2022 | null |
XYLayoutLM: Towards Layout-Aware Multimodal Networks for Visually-Rich Document Understanding | Zhangxuan Gu, Changhua Meng, Ke Wang, Jun Lan, Weiqiang Wang, Ming Gu, Liqing Zhang | Recently, various multimodal networks for Visually-Rich Document Understanding(VRDU) have been proposed, showing the promotion of transformers by integrating visual and layout information with the text embeddings. However, most existing approaches utilize the position embeddings to incorporate the sequence information, neglecting the noisy improper reading order obtained by OCR tools. In this paper, we propose a robust layout-aware multimodal network named XYLayoutLM to capture and leverage rich layout information from proper reading orders produced by our Augmented XY Cut. Moreover, a Dilated Conditional Position Encoding module is proposed to deal with the input sequence of variable lengths, and it additionally extracts local layout information from both textual and visual modalities while generating position embeddings. Experiment results show that our XYLayoutLM achieves competitive results on document understanding tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gu_XYLayoutLM_Towards_Layout-Aware_Multimodal_Networks_for_Visually-Rich_Document_Understanding_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gu_XYLayoutLM_Towards_Layout-Aware_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.06947 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gu_XYLayoutLM_Towards_Layout-Aware_Multimodal_Networks_for_Visually-Rich_Document_Understanding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gu_XYLayoutLM_Towards_Layout-Aware_Multimodal_Networks_for_Visually-Rich_Document_Understanding_CVPR_2022_paper.html | CVPR 2022 | null |
Amodal Segmentation Through Out-of-Task and Out-of-Distribution Generalization With a Bayesian Model | Yihong Sun, Adam Kortylewski, Alan Yuille | Amodal completion is a visual task that humans perform easily but which is difficult for computer vision algorithms. The aim is to segment those object boundaries which are occluded and hence invisible. This task is particularly challenging for deep neural networks because data is difficult to obtain and annotate. Therefore, we formulate amodal segmentation as an out-of-task and out-of-distribution generalization problem. Specifically, we replace the fully connected classifier in neural networks with a Bayesian generative model of the neural network features. The model is trained from non-occluded images using bounding box annotations and class labels only, but is applied to generalize out-of-task to object segmentation and to generalize out-of-distribution to segment occluded objects. We demonstrate how such Bayesian models can naturally generalize beyond the training task labels when they learn a prior that models the object's background context and shape. Moreover, by leveraging an outlier process, Bayesian models can further generalize out-of-distribution to segment partially occluded objects and to predict their amodal object boundaries. Our algorithm outperforms alternative methods that use the same supervision by a large margin, and even outperforms methods where annotated amodal segmentations are used during training, when the amount of occlusion is large. Code is publically available at https://github.com/anonymous-submission-vision/Amodal-Bayesian. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_Amodal_Segmentation_Through_Out-of-Task_and_Out-of-Distribution_Generalization_With_a_Bayesian_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sun_Amodal_Segmentation_Through_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2010.13175 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Amodal_Segmentation_Through_Out-of-Task_and_Out-of-Distribution_Generalization_With_a_Bayesian_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Amodal_Segmentation_Through_Out-of-Task_and_Out-of-Distribution_Generalization_With_a_Bayesian_CVPR_2022_paper.html | CVPR 2022 | null |
How Well Do Sparse ImageNet Models Transfer? | Eugenia Iofinova, Alexandra Peste, Mark Kurtz, Dan Alistarh | Transfer learning is a classic paradigm by which models pretrained on large "upstream" datasets are adapted to yield good results on "downstream" specialized datasets. Generally, more accurate models on the "upstream" dataset tend to provide better transfer accuracy "downstream". In this work, we perform an in-depth investigation of this phenomenon in the context of convolutional neural networks (CNNs) trained on the ImageNet dataset, which have been pruned--that is, compressed by sparsifiying their connections. We consider transfer using unstructured pruned models obtained by applying several state-of-the-art pruning methods, including magnitude-based, second-order, re-growth, lottery-ticket, and regularization approaches, in the context of twelve standard transfer tasks. In a nutshell, our study shows that sparse models can match or even outperform the transfer performance of dense models, even at high sparsities, and, while doing so, can lead to significant inference and even training speedups. At the same time, we observe and analyze significant differences in the behaviour of different pruning methods. The code is available at: https://github.com/IST-DASLab/sparse-imagenet-transfer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Iofinova_How_Well_Do_Sparse_ImageNet_Models_Transfer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Iofinova_How_Well_Do_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.13445 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Iofinova_How_Well_Do_Sparse_ImageNet_Models_Transfer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Iofinova_How_Well_Do_Sparse_ImageNet_Models_Transfer_CVPR_2022_paper.html | CVPR 2022 | null |
REX: Reasoning-Aware and Grounded Explanation | Shi Chen, Qi Zhao | Effectiveness and interpretability are two essential properties for trustworthy AI systems. Most recent studies in visual reasoning are dedicated to improving the accuracy of predicted answers, and less attention is paid to explaining the rationales behind the decisions. As a result, they commonly take advantage of spurious biases instead of actually reasoning on the visual-textual data, and have yet developed the capability to explain their decision making by considering key information from both modalities. This paper aims to close the gap from three distinct perspectives: first, we define a new type of multi-modal explanations that explain the decisions by progressively traversing the reasoning process and grounding keywords in the images. We develop a functional program to sequentially execute different reasoning steps and construct a new dataset with 1,040,830 multi-modal explanations. Second, we identify the critical need to tightly couple important components across the visual and textual modalities for explaining the decisions, and propose a novel explanation generation method that explicitly models the pairwise correspondence between words and regions of interest. It improves the visual grounding capability by a considerable margin, resulting in enhanced interpretability and reasoning performance. Finally, with our new data and method, we perform extensive analyses to study the effectiveness of our explanation under different settings, including multi-task learning and transfer learning. Our code and data are available at https://github.com/szzexpoi/rex | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_REX_Reasoning-Aware_and_Grounded_Explanation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_REX_Reasoning-Aware_and_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.06107 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_REX_Reasoning-Aware_and_Grounded_Explanation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_REX_Reasoning-Aware_and_Grounded_Explanation_CVPR_2022_paper.html | CVPR 2022 | null |
Dynamic Dual-Output Diffusion Models | Yaniv Benny, Lior Wolf | Iterative denoising-based generation, also known as denoising diffusion models, has recently been shown to be comparable in quality to other classes of generative models, and even surpass them. Including, in particular, Generative Adversarial Networks, which are currently the state of the art in many sub-tasks of image generation. However, a major drawback of this method is that it requires hundreds of iterations to produce a competitive result. Recent works have proposed solutions that allow for faster generation with fewer iterations, but the image quality gradually deteriorates with increasingly fewer iterations being applied during generation. In this paper, we reveal some of the causes that affect the generation quality of diffusion models, especially when sampling with few iterations, and come up with a simple, yet effective, solution to mitigate them. We consider two opposite equations for the iterative denoising, the first predicts the applied noise, and the second predicts the image directly. Our solution takes the two options and learns to dynamically alternate between them through the denoising process. Our proposed solution is general and can be applied to any existing diffusion model. As we show, when applied to various SOTA architectures, our solution immediately improves their generation quality, with negligible added complexity and parameters. We experiment on multiple datasets and configurations and run an extensive ablation study to support these findings. | https://openaccess.thecvf.com/content/CVPR2022/papers/Benny_Dynamic_Dual-Output_Diffusion_Models_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Benny_Dynamic_Dual-Output_Diffusion_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.04304 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Benny_Dynamic_Dual-Output_Diffusion_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Benny_Dynamic_Dual-Output_Diffusion_Models_CVPR_2022_paper.html | CVPR 2022 | null |
StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis | Zhiheng Li, Martin Renqiang Min, Kai Li, Chenliang Xu | Although progress has been made for text-to-image synthesis, previous methods fall short of generalizing to unseen or underrepresented attribute compositions in the input text. Lacking compositionality could have severe implications for robustness and fairness, e.g., inability to synthesize the face images of underrepresented demographic groups. In this paper, we introduce a new framework, StyleT2I, to improve the compositionality of text-to-image synthesis. Specifically, we propose a CLIP-guided Contrastive Loss to better distinguish different compositions among different sentences. To further improve the compositionality, we design a novel Semantic Matching Loss and a Spatial Constraint to identify attributes' latent directions for intended spatial region manipulations, leading to better disentangled latent representations of attributes. Based on the identified latent directions of attributes, we propose Compositional Attribute Adjustment to adjust the latent code, resulting in better compositionality of image synthesis. In addition, we leverage the l_2-norm regularization of identified latent directions (norm penalty) to strike a nice balance between image-text alignment and image fidelity. In the experiments, we devise a new dataset split and an evaluation metric to evaluate the compositionality of text-to-image synthesis models. The results show that StyleT2I outperforms previous approaches in terms of the consistency between the input text and synthesized images and achieves higher fidelity. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_StyleT2I_Toward_Compositional_and_High-Fidelity_Text-to-Image_Synthesis_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_StyleT2I_Toward_Compositional_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15799 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_StyleT2I_Toward_Compositional_and_High-Fidelity_Text-to-Image_Synthesis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_StyleT2I_Toward_Compositional_and_High-Fidelity_Text-to-Image_Synthesis_CVPR_2022_paper.html | CVPR 2022 | null |
JoinABLe: Learning Bottom-Up Assembly of Parametric CAD Joints | Karl D.D. Willis, Pradeep Kumar Jayaraman, Hang Chu, Yunsheng Tian, Yifei Li, Daniele Grandi, Aditya Sanghi, Linh Tran, Joseph G. Lambourne, Armando Solar-Lezama, Wojciech Matusik | Physical products are often complex assemblies combining a multitude of 3D parts modeled in computer-aided design (CAD) software. CAD designers build up these assemblies by aligning individual parts to one another using constraints called joints. In this paper we introduce JoinABLe, a learning-based method that assembles parts together to form joints. JoinABLe uses the weak supervision available in standard parametric CAD files without the help of object class labels or human guidance. Our results show that by making network predictions over a graph representation of solid models we can outperform multiple baseline methods with an accuracy (79.53%) that approaches human performance (80%). Finally, to support future research we release the AssemblyJoint dataset, containing assemblies with rich information on joints, contact surfaces, holes, and the underlying assembly graph structure. | https://openaccess.thecvf.com/content/CVPR2022/papers/Willis_JoinABLe_Learning_Bottom-Up_Assembly_of_Parametric_CAD_Joints_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Willis_JoinABLe_Learning_Bottom-Up_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.12772 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Willis_JoinABLe_Learning_Bottom-Up_Assembly_of_Parametric_CAD_Joints_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Willis_JoinABLe_Learning_Bottom-Up_Assembly_of_Parametric_CAD_Joints_CVPR_2022_paper.html | CVPR 2022 | null |
CaDeX: Learning Canonical Deformation Coordinate Space for Dynamic Surface Representation via Neural Homeomorphism | Jiahui Lei, Kostas Daniilidis | While neural representations for static 3D shapes are widely studied, representations for deformable surfaces are limited to be template-dependent or to lack efficiency. We introduce Canonical Deformation Coordinate Space (CaDeX), a unified representation of both shape and nonrigid motion. Our key insight is the factorization of the deformation between frames by continuous bijective canonical maps (homeomorphisms) and their inverses that go through a learned canonical shape. Our novel deformation representation and its implementation are simple, efficient, and guarantee cycle consistency, topology preservation, and, if needed, volume conservation. Our modelling of the learned canonical shapes provides a flexible and stable space for shape prior learning. We demonstrate state-of-the-art performance in modelling a wide range of deformable geometries: human bodies, animal bodies, and articulated objects. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lei_CaDeX_Learning_Canonical_Deformation_Coordinate_Space_for_Dynamic_Surface_Representation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lei_CaDeX_Learning_Canonical_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16529 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lei_CaDeX_Learning_Canonical_Deformation_Coordinate_Space_for_Dynamic_Surface_Representation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lei_CaDeX_Learning_Canonical_Deformation_Coordinate_Space_for_Dynamic_Surface_Representation_CVPR_2022_paper.html | CVPR 2022 | null |
Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes | Yang You, Zelin Ye, Yujing Lou, Chengkun Li, Yong-Lu Li, Lizhuang Ma, Weiming Wang, Cewu Lu | 3D object detection has attracted much attention thanks to the advances in sensors and deep learning methods for point clouds. Current state-of-the-art methods like VoteNet regress direct offset towards object centers and box orientations with an additional Multi-Layer-Perceptron network. Both their offset and orientation predictions are not accurate due to the fundamental difficulty in rotation classification. In the work, we disentangle the direct offset into Local Canonical Coordinates (LCC), box scales and box orientations. Only LCC and box scales are regressed, while box orientations are generated by a canonical voting scheme. Finally, an LCC-aware back-projection checking algorithm iteratively cuts out bounding boxes from the generated vote maps, with the elimination of false positives. Our model achieves state-of-the-art performance on three standard real-world benchmarks: ScanNet, SceneNN and SUN RGB-D. Our code is available on https://github.com/qq456cvb/CanonicalVoting. | https://openaccess.thecvf.com/content/CVPR2022/papers/You_Canonical_Voting_Towards_Robust_Oriented_Bounding_Box_Detection_in_3D_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/You_Canonical_Voting_Towards_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2011.12001 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/You_Canonical_Voting_Towards_Robust_Oriented_Bounding_Box_Detection_in_3D_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/You_Canonical_Voting_Towards_Robust_Oriented_Bounding_Box_Detection_in_3D_CVPR_2022_paper.html | CVPR 2022 | null |
V-Doc: Visual Questions Answers With Documents | Yihao Ding, Zhe Huang, Runlin Wang, YanHang Zhang, Xianru Chen, Yuzhong Ma, Hyunsuk Chung, Soyeon Caren Han | We propose V-Doc, a question-answering tool using document images and PDF, mainly for researchers and general non-deep learning experts looking to generate, process, and understand the document visual question answering tasks. The V-Doc supports generating and using both extractive and abstractive question-answer pairs using documents images. The extractive QA selects a subset of tokens or phrases from the document contents to predict the answers, while the abstractive QA recognises the language in the content and generates the answer based on the trained model. Both aspects are crucial to understanding the documents, especially in an image format. We include a detailed scenario of question generation for the abstractive QA task. V-Doc supports a wide range of datasets and models, and is highly extensible through a declarative, framework-agnostic platform. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ding_V-Doc_Visual_Questions_Answers_With_Documents_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_V-Doc_Visual_Questions_Answers_With_Documents_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_V-Doc_Visual_Questions_Answers_With_Documents_CVPR_2022_paper.html | CVPR 2022 | null |
AEGNN: Asynchronous Event-Based Graph Neural Networks | Simon Schaefer, Daniel Gehrig, Davide Scaramuzza | The best performing learning algorithms devised for event cameras work by first converting events into dense representations that are then processed using standard CNNs. However, these steps discard both the sparsity and high temporal resolution of events, leading to high computational burden and latency. For this reason, recent works have adopted Graph Neural Networks (GNNs), which process events as "static" spatio-temporal graphs, which are inherently "sparse". We take this trend one step further by introducing Asynchronous, Event-based Graph Neural Networks (AEGNNs), a novel event-processing paradigm that generalizes standard GNNs to process events as "evolving" spatio-temporal graphs. AEGNNs follow efficient update rules that restrict recomputation of network activations only to the nodes affected by each new event, thereby significantly reducing both computation and latency for event-by-event processing. AEGNNs are easily trained on synchronous inputs and can be converted to efficient, "asynchronous" networks at test time. We thoroughly validate our method on object classification and detection tasks, where we show an up to a 200-fold reduction in computational complexity (FLOPs), with similar or even better performance than state-of-the-art asynchronous methods. This reduction in computation directly translates to an 8-fold reduction in computational latency when compared to standard GNNs, which opens the door to low-latency event-based processing. | https://openaccess.thecvf.com/content/CVPR2022/papers/Schaefer_AEGNN_Asynchronous_Event-Based_Graph_Neural_Networks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Schaefer_AEGNN_Asynchronous_Event-Based_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.17149 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Schaefer_AEGNN_Asynchronous_Event-Based_Graph_Neural_Networks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Schaefer_AEGNN_Asynchronous_Event-Based_Graph_Neural_Networks_CVPR_2022_paper.html | CVPR 2022 | null |
Layer-Wised Model Aggregation for Personalized Federated Learning | Xiaosong Ma, Jie Zhang, Song Guo, Wenchao Xu | Personalized Federated Learning (pFL) not only can capture the common priors from broad range of distributed data, but also support customized models for heterogeneous clients. Researches over the past few years have applied the weighted aggregation manner to produce personalized models, where the weights are determined by calibrating the distance of the entire model parameters or loss values, and have yet to consider the layer-level impacts to the aggregation process, leading to lagged model convergence and inadequate personalization over non-IID datasets. In this paper, we propose a novel pFL training framework dubbed Layer-wised Personalized Federated learning (pFedLA) that can discern the importance of each layer from different clients, and thus is able to optimize the personalized model aggregation for clients with heterogeneous data. Specifically, we employ a dedicated hypernetwork per client on the server side, which is trained to identify the mutual contribution factors at layer granularity. Meanwhile, a parameterized mechanism is introduced to update the layer-wised aggregation weights to progressively exploit the inter-user similarity and realize accurate model personalization. Extensive experiments are conducted over different models and learning tasks, and we show that the proposed methods achieve significantly higher performance than state-of-the-art pFL methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ma_Layer-Wised_Model_Aggregation_for_Personalized_Federated_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ma_Layer-Wised_Model_Aggregation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.03993 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Layer-Wised_Model_Aggregation_for_Personalized_Federated_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Layer-Wised_Model_Aggregation_for_Personalized_Federated_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values | Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk | We present Polarity Sampling, a theoretically justified plug-and-play method for controlling the generation quality and diversity of any pre-trained deep generative network (DGN). Leveraging the fact that DGNs are, or can be approximated by, continuous piecewise affine splines, we derive the analytical DGN output space distribution as a function of the product of the DGN's Jacobian singular values raised to a power rho. We dub rho the polarity parameter and prove that rho focuses the DGN sampling on the modes (rho < 0) or anti-modes (rho > 0) of the DGN output space probability distribution. We demonstrate that nonzero polarity values achieve a better precision-recall (quality-diversity) Pareto frontier than standard methods, such as truncation, for a number of state-of-the-art DGNs. We also present quantitative and qualitative results on the improvement of overall generation quality (e.g., in terms of the Frechet Inception Distance) for a number of state-of-the-art DGNs, including StyleGAN3, BigGAN-deep, NVAE, for different conditional and unconditional image generation tasks. In particular, Polarity Sampling redefines the state-of-the-art for StyleGAN2 on the FFHQ Dataset to FID 2.57, StyleGAN2 on the LSUN Car Dataset to FID 2.27 and StyleGAN3 on the AFHQv2 Dataset to FID 3.95. Colab Demo: bit.ly/polarity-samp | https://openaccess.thecvf.com/content/CVPR2022/papers/Humayun_Polarity_Sampling_Quality_and_Diversity_Control_of_Pre-Trained_Generative_Networks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Humayun_Polarity_Sampling_Quality_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01993 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Humayun_Polarity_Sampling_Quality_and_Diversity_Control_of_Pre-Trained_Generative_Networks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Humayun_Polarity_Sampling_Quality_and_Diversity_Control_of_Pre-Trained_Generative_Networks_CVPR_2022_paper.html | CVPR 2022 | null |
Style-Structure Disentangled Features and Normalizing Flows for Diverse Icon Colorization | Yuan-kui Li, Yun-Hsuan Lien, Yu-Shuen Wang | In this study, we present a colorization network that generates flat-color icons according to given sketches and semantic colorization styles. Specifically, our network contains a style-structure disentangled colorization module and a normalizing flow. The colorization module transforms a paired sketch image and style image into a flat-color icon. To enhance network generalization and the quality of icons, we present a pixel-wise decoder, a global style code, and a contour loss to reduce color gradients at flat regions and increase color discontinuity at boundaries. The normalizing flow maps Gaussian vectors to diverse style codes conditioned on the given semantic colorization label. This conditional sampling enables users to control attributes and obtain diverse colorization results. Compared to previous colorization methods built upon conditional generative adversarial networks, our approach enjoys the advantages of both high image quality and diversity. To evaluate its effectiveness, we compared the flat-color icons generated by our approach and recent colorization and image-to-image translation methods on various conditions. Experiment results verify that our method outperforms state-of-the-arts qualitatively and quantitatively. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Style-Structure_Disentangled_Features_and_Normalizing_Flows_for_Diverse_Icon_Colorization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Style-Structure_Disentangled_Features_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Style-Structure_Disentangled_Features_and_Normalizing_Flows_for_Diverse_Icon_Colorization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Style-Structure_Disentangled_Features_and_Normalizing_Flows_for_Diverse_Icon_Colorization_CVPR_2022_paper.html | CVPR 2022 | null |
Object-Aware Video-Language Pre-Training for Retrieval | Jinpeng Wang, Yixiao Ge, Guanyu Cai, Rui Yan, Xudong Lin, Ying Shan, Xiaohu Qie, Mike Zheng Shou | Recently, by introducing large-scale dataset and strong transformer network, video-language pre-training has shown great success especially for retrieval. Yet, existing video-language transformer models do not explicitly fine-grained semantic align. In this work, we present Object-aware Transformers, an object-centric approach that extends video-language transformer to incorporate object representations. The key idea is to leverage the bounding boxes and object tags to guide the training process. We evaluate our model on three standard sub-tasks of video-text matching on four widely used benchmarks. We also provide deep analysis and detailed ablation about the proposed method. We show clear improvement in performance across all tasks and datasets considered, demonstrating the value of a model that incorporates object representations into a video-language architecture. The code has been released in https://github.com/FingerRec/OA-Transformer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Object-Aware_Video-Language_Pre-Training_for_Retrieval_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2112.00656 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Object-Aware_Video-Language_Pre-Training_for_Retrieval_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Object-Aware_Video-Language_Pre-Training_for_Retrieval_CVPR_2022_paper.html | CVPR 2022 | null |
OSKDet: Orientation-Sensitive Keypoint Localization for Rotated Object Detection | Dongchen Lu, Dongmei Li, Yali Li, Shengjin Wang | Rotated object detection is a challenging issue in computer vision field. Inadequate rotated representation and the confusion of parametric regression have been the bottleneck for high performance rotated detection. In this paper, we propose an orientation-sensitive keypoint based rotated detector OSKDet. First, we adopt a set of keypoints to represent the target and predict the keypoint heatmap on ROI to get the rotated box. By proposing the orientation-sensitive heatmap, OSKDet could learn the shape and direction of rotated target implicitly and has stronger modeling capabilities for rotated representation, which improves the localization accuracy and acquires high quality detection results. Second, we explore a new unordered keypoint representation paradigm, which could avoid the confusion of keypoint regression caused by rule based ordering. Furthermore, we propose a localization quality uncertainty module to better predict the classification score by the distribution uncertainty of keypoints heatmap. Experimental results on several public benchmarks show the state-of-the-art performance of OSKDet. Specifically, we achieve an AP of 80.91% on DOTA, 89.98% on HRSC2016, 97.27% on UCAS-AOD, and a F-measure of 92.18% on ICDAR2015, 81.43% on ICDAR2017, respectively. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lu_OSKDet_Orientation-Sensitive_Keypoint_Localization_for_Rotated_Object_Detection_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lu_OSKDet_Orientation-Sensitive_Keypoint_Localization_for_Rotated_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lu_OSKDet_Orientation-Sensitive_Keypoint_Localization_for_Rotated_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
MAT: Mask-Aware Transformer for Large Hole Image Inpainting | Wenbo Li, Zhe Lin, Kun Zhou, Lu Qi, Yi Wang, Jiaya Jia | Recent studies have shown the importance of modeling long-range interactions in the inpainting problem. To achieve this goal, existing approaches exploit either standalone attention techniques or transformers, but usually under a low resolution in consideration of computational cost. In this paper, we present a novel transformer-based model for large hole inpainting, which unifies the merits of transformers and convolutions to efficiently process high-resolution images. We carefully design each component of our framework to guarantee the high fidelity and diversity of recovered images. Specifically, we customize an inpainting-oriented transformer block, where the attention module aggregates non-local information only from partial valid tokens, indicated by a dynamic mask. Extensive experiments demonstrate the state-of-the-art performance of the new model on multiple benchmark datasets. Code is released at https://github.com/fenglinglwb/MAT. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_MAT_Mask-Aware_Transformer_for_Large_Hole_Image_Inpainting_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_MAT_Mask-Aware_Transformer_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.15270 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_MAT_Mask-Aware_Transformer_for_Large_Hole_Image_Inpainting_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_MAT_Mask-Aware_Transformer_for_Large_Hole_Image_Inpainting_CVPR_2022_paper.html | CVPR 2022 | null |
Exploring Geometric Consistency for Monocular 3D Object Detection | Qing Lian, Botao Ye, Ruijia Xu, Weilong Yao, Tong Zhang | This paper investigates the geometric consistency for monocular 3D object detection, which suffers from the ill-posed depth estimation. We first conduct a thorough analysis to reveal how existing methods fail to consistently localize objects when different geometric shifts occur. In particular, we design a series of geometric manipulations to diagnose existing detectors and then illustrate their vulnerability to consistently associate the depth with object apparent sizes and positions. To alleviate this issue, we propose four geometry-aware data augmentation approaches to enhance the geometric consistency of the detectors. We first modify some commonly used data augmentation methods for 2D images so that they can maintain geometric consistency in 3D spaces. We demonstrate such modifications are important. In addition, we propose a 3D-specific image perturbation method that employs the camera movement. During the augmentation process, the camera system with the corresponding image is manipulated, while the geometric visual cues for depth recovery are preserved. We show that by using the geometric consistency constraints, the proposed augmentation techniques lead to improvements on the KITTI and nuScenes monocular 3D detection benchmarks with state-of-the-art results. In addition, we demonstrate that the augmentation methods are well suited for semi-supervised training and cross-dataset generalization. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lian_Exploring_Geometric_Consistency_for_Monocular_3D_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lian_Exploring_Geometric_Consistency_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2104.05858 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lian_Exploring_Geometric_Consistency_for_Monocular_3D_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lian_Exploring_Geometric_Consistency_for_Monocular_3D_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Window Fully-Connected CRFs for Monocular Depth Estimation | Weihao Yuan, Xiaodong Gu, Zuozhuo Dai, Siyu Zhu, Ping Tan | Estimating the accurate depth from a single image is challenging since it is inherently ambiguous and ill-posed. While recent works design increasingly complicated and powerful networks to directly regress the depth map, we take the path of CRFs optimization. Due to the expensive computation, CRFs are usually performed between neighborhoods rather than the whole graph. To leverage the potential of fully-connected CRFs, we split the input into windows and perform the FC-CRFs optimization within each window, which reduces the computation complexity and makes FC-CRFs feasible. To better capture the relationships between nodes in the graph, we exploit the multi-head attention mechanism to compute a multi-head potential function, which is fed to the networks to output an optimized depth map. Then we build a bottom-up-top-down structure, where this neural window FC-CRFs module serves as the decoder, and a vision transformer serves as the encoder. The experiments demonstrate that our method significantly improves the performance across all metrics on both the KITTI and NYUv2 datasets, compared to previous methods. Furthermore, the proposed method can be directly applied to panorama images and outperforms all previous panorama methods on the MatterPort3D dataset. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yuan_Neural_Window_Fully-Connected_CRFs_for_Monocular_Depth_Estimation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.01502 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yuan_Neural_Window_Fully-Connected_CRFs_for_Monocular_Depth_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yuan_Neural_Window_Fully-Connected_CRFs_for_Monocular_Depth_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
CodedVTR: Codebook-Based Sparse Voxel Transformer With Geometric Guidance | Tianchen Zhao, Niansong Zhang, Xuefei Ning, He Wang, Li Yi, Yu Wang | Transformers have gained much attention by outperforming convolutional neural networks in many 2D vision tasks. However, they are known to have generalization problems and rely on massive-scale pre-training and sophisticated training techniques. When applying to 3D tasks, the irregular data structure and limited data scale add to the difficulty of transformer's application. We propose Codebook-based Voxel TRansformer), which improves data efficiency and generalization ability for 3D sparse voxel transformers. On the one hand, we propose the codebook-based attention that projects an attention space into its subspace represented by the combination of "prototypes" in a learnable codebook. It regularizes attention learning and improves generalization. On the other hand, we propose geometry-aware self-attention that utilizes geometric information (geometric pattern, density) to guide attention learning. CodedVTR could be embedded into existing sparse convolution-based methods, and bring consistent performance improvements for indoor and outdoor 3D semantic segmentation tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_CodedVTR_Codebook-Based_Sparse_Voxel_Transformer_With_Geometric_Guidance_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhao_CodedVTR_Codebook-Based_Sparse_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09887 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_CodedVTR_Codebook-Based_Sparse_Voxel_Transformer_With_Geometric_Guidance_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_CodedVTR_Codebook-Based_Sparse_Voxel_Transformer_With_Geometric_Guidance_CVPR_2022_paper.html | CVPR 2022 | null |
Uncertainty-Aware Deep Multi-View Photometric Stereo | Berk Kaya, Suryansh Kumar, Carlos Oliveira, Vittorio Ferrari, Luc Van Gool | This paper presents a simple and effective solution to the longstanding classical multi-view photometric stereo (MVPS) problem. It is well-known that photometric stereo (PS) is excellent at recovering high-frequency surface details, whereas multi-view stereo (MVS) can help remove the low-frequency distortion due to PS and retain the global geometry of the shape. This paper proposes an approach that can effectively utilize such complementary strengths of PS and MVS. Our key idea is to combine them suitably while considering the per-pixel uncertainty of their estimates. To this end, we estimate per-pixel surface normals and depth using an uncertainty-aware deep-PS network and deep-MVS network, respectively. Uncertainty modeling helps select reliable surface normal and depth estimates at each pixel which then act as a true representative of the dense surface geometry. At each pixel, our approach either selects or discards deep-PS and deep-MVS network prediction depending on the prediction uncertainty measure. For dense, detailed, and precise inference of the object's surface profile, we propose to learn the implicit neural shape representation via a multilayer perceptron (MLP). Our approach encourages the MLP to converge to a natural zero-level set surface using the confident prediction from deep-PS and deep-MVS networks, providing superior dense surface reconstruction. Extensive experiments on the DiLiGenT-MV benchmark dataset show that our method provides high-quality shape recovery with a much lower memory footprint while outperforming almost all of the existing approaches. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kaya_Uncertainty-Aware_Deep_Multi-View_Photometric_Stereo_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kaya_Uncertainty-Aware_Deep_Multi-View_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2202.13071 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kaya_Uncertainty-Aware_Deep_Multi-View_Photometric_Stereo_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kaya_Uncertainty-Aware_Deep_Multi-View_Photometric_Stereo_CVPR_2022_paper.html | CVPR 2022 | null |
Coherent Point Drift Revisited for Non-Rigid Shape Matching and Registration | Aoxiang Fan, Jiayi Ma, Xin Tian, Xiaoguang Mei, Wei Liu | In this paper, we explore a new type of extrinsic method to directly align two geometric shapes with point-to-point correspondences in ambient space by recovering a deformation, which allows more continuous and smooth maps to be obtained. Specifically, the classic coherent point drift is revisited and generalizations have been proposed. First, by observing that the deformation model is essentially defined with respect to Euclidean space, we generalize the kernel method to non-Euclidean domains. This generally leads to better results for processing shapes, which are known as two-dimensional manifolds. Second, a generalized probabilistic model is proposed to address the sensibility of coherent point drift method to local optima. Instead of directly optimizing over the objective of coherent point drift, the new model allows to focus on a group of most confident ones, thus improves the robustness of the registration system. Experiments are conducted on multiple public datasets with comparison to state-of-the-art competitors, demonstrating the superiority of our method which is both flexible and efficient to improve the matching accuracy due to our extrinsic alignment objective in ambient space. | https://openaccess.thecvf.com/content/CVPR2022/papers/Fan_Coherent_Point_Drift_Revisited_for_Non-Rigid_Shape_Matching_and_Registration_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_Coherent_Point_Drift_Revisited_for_Non-Rigid_Shape_Matching_and_Registration_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_Coherent_Point_Drift_Revisited_for_Non-Rigid_Shape_Matching_and_Registration_CVPR_2022_paper.html | CVPR 2022 | null |
Unleashing Potential of Unsupervised Pre-Training With Intra-Identity Regularization for Person Re-Identification | Zizheng Yang, Xin Jin, Kecheng Zheng, Feng Zhao | Existing person re-identification (ReID) methods typically directly load the pre-trained ImageNet weights for initialization. However, as a fine-grained classification task, ReID is more challenging and exists a large domain gap between ImageNet classification. Inspired by the great success of self-supervised representation learning with contrastive objectives, in this paper, we design an Unsupervised Pre-training framework for ReID based on the contrastive learning (CL) pipeline, dubbed UP-ReID. During the pre-training, we attempt to address two critical issues for learning fine-grained ReID features: (1) the augmentations in CL pipeline may distort the discriminative clues in person images. (2) the fine-grained local features of person images are not fully-explored. Therefore, we introduce an (I^2-)regularization in the UP-ReID, which is instantiated as two constraints coming from global image aspect and local patch aspect: a global consistency is enforced between augmented and original person images to increase robustness to augmentation, while an intrinsic contrastive constraint among local patches of each image is employed to fully explore the local discriminative clues. Extensive experiments on multiple popular Re-ID datasets, including PersonX, Market1501, CUHK03, and MSMT17, demonstrate that our UP-ReID pre-trained model can significantly benefit the downstream ReID fine-tuning and achieve state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Unleashing_Potential_of_Unsupervised_Pre-Training_With_Intra-Identity_Regularization_for_Person_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Unleashing_Potential_of_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Unleashing_Potential_of_Unsupervised_Pre-Training_With_Intra-Identity_Regularization_for_Person_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Unleashing_Potential_of_Unsupervised_Pre-Training_With_Intra-Identity_Regularization_for_Person_CVPR_2022_paper.html | CVPR 2022 | null |
Align and Prompt: Video-and-Language Pre-Training With Entity Prompts | Dongxu Li, Junnan Li, Hongdong Li, Juan Carlos Niebles, Steven C.H. Hoi | Video-and-language pre-training has shown promising improvements on various downstream tasks. Most previous methods capture cross-modal interactions with a transformer-based multimodal encoder, not fully addressing the misalignment between unimodal video and text features. Besides, learning fine-grained visual-language alignment usually requires off-the-shelf object detectors to provide object information, which is bottlenecked by the detector's limited vocabulary and expensive computation cost. We propose Align and Prompt: an efficient and effective video-and-language pre-training framework with better cross-modal alignment. First, we introduce a video-text contrastive (VTC) loss to align unimodal video-text features at the instance level, which eases the modeling of cross-modal interactions. Then, we propose a new visually-grounded pre-training task, prompting entity modeling (PEM), which aims to learn fine-grained region-entity alignment. To achieve this, we first introduce an entity prompter module, which is trained with VTC to produce the similarity between a video crop and text prompts instantiated with entity names. The PEM task then asks the model to predict the entity pseudo-labels (i.e normalized similarity scores) for randomly-selected video crops. The resulting pre-trained model achieves state-of-the-art performance on both text-video retrieval and videoQA, outperforming prior work by a substantial margin. Our code and pre-trained models are available at https://github.com/salesforce/ALPRO. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Align_and_Prompt_Video-and-Language_Pre-Training_With_Entity_Prompts_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2112.09583 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Align_and_Prompt_Video-and-Language_Pre-Training_With_Entity_Prompts_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Align_and_Prompt_Video-and-Language_Pre-Training_With_Entity_Prompts_CVPR_2022_paper.html | CVPR 2022 | null |
A Unified Query-Based Paradigm for Point Cloud Understanding | Zetong Yang, Li Jiang, Yanan Sun, Bernt Schiele, Jiaya Jia | 3D point cloud understanding is an important component in autonomous driving and robotics. In this paper, we present a novel Embedding-Querying paradigm (EQ- Paradigm) for 3D understanding tasks including detection, segmentation and classification. EQ-Paradigm is a unified paradigm that enables combination of existing 3D backbone architectures with different task heads. Under the EQ- Paradigm, the input is first encoded in the embedding stage with an arbitrary feature extraction architecture, which is independent of tasks and heads. Then, the querying stage enables the encoded features for diverse task heads. This is achieved by introducing an intermediate representation, i.e., Q-representation, in the querying stage to bridge the embedding stage and task heads. We design a novel Q-Net as the querying stage network. Extensive experimental results on various 3D tasks show that EQ-Paradigm in tandem with Q-Net is a general and effective pipeline, which enables flexible collaboration of backbones and heads. It further boosts performance of state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_A_Unified_Query-Based_Paradigm_for_Point_Cloud_Understanding_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_A_Unified_Query-Based_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01252 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_A_Unified_Query-Based_Paradigm_for_Point_Cloud_Understanding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_A_Unified_Query-Based_Paradigm_for_Point_Cloud_Understanding_CVPR_2022_paper.html | CVPR 2022 | null |
It's About Time: Analog Clock Reading in the Wild | Charig Yang, Weidi Xie, Andrew Zisserman | In this paper, we present a framework for reading analog clocks in natural images or videos. Specifically, we make the following contributions: First, we create a scalable pipeline for generating synthetic clocks, significantly reducing the requirements for the labour-intensive annotations; Second, we introduce a clock recognition architecture based on spatial transformer networks (STN), which is trained end-to-end for clock alignment and recognition. We show that the model trained on the proposed synthetic dataset generalises towards real clocks with good accuracy, advocating a Sim2Real training regime; Third, to further reduce the gap between simulation and real data, we leverage the special property of "time", i.e.uniformity, to generate reliable pseudo-labels on real unlabelled clock videos, and show that training on these videos offers further improvements while still requiring zero manual annotations. Lastly, we introduce three benchmark datasets based on COCO, Open Images, and The Clock movie, with full annotations for time, accurate to the minute. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Its_About_Time_Analog_Clock_Reading_in_the_Wild_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Its_About_Time_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Its_About_Time_Analog_Clock_Reading_in_the_Wild_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Its_About_Time_Analog_Clock_Reading_in_the_Wild_CVPR_2022_paper.html | CVPR 2022 | null |
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