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BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning | Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, Trevor Darrell | Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Researchers are usually constrained to study a small set of problems on one dataset, while real-world computer vision applications require performing tasks of various complexities. We construct BDD100K, the largest driving video dataset with 100K videos and 10 tasks to evaluate the exciting progress of image recognition algorithms on autonomous driving. The dataset possesses geographic, environmental, and weather diversity, which is useful for training models that are less likely to be surprised by new conditions. Based on this diverse dataset, we build a benchmark for heterogeneous multitask learning and study how to solve the tasks together. Our experiments show that special training strategies are needed for existing models to perform such heterogeneous tasks. BDD100K opens the door for future studies in this important venue. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yu_BDD100K_A_Diverse_Driving_Dataset_for_Heterogeneous_Multitask_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/1805.04687 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_BDD100K_A_Diverse_Driving_Dataset_for_Heterogeneous_Multitask_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_BDD100K_A_Diverse_Driving_Dataset_for_Heterogeneous_Multitask_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yu_BDD100K_A_Diverse_CVPR_2020_supplemental.pdf | https://cove.thecvf.com/datasets/354 | null |
Plug-and-Play Algorithms for Large-Scale Snapshot Compressive Imaging | Xin Yuan, Yang Liu, Jinli Suo, Qionghai Dai | Snapshot compressive imaging (SCI) aims to capture the high-dimensional (usually 3D) images using a 2D sensor (detector) in a single snapshot. Though enjoying the advantages of low-bandwidth, low-power and low-cost, applying SCI to large-scale problems (HD or UHD videos) in our daily life is still challenging. The bottleneck lies in the reconstruction algorithms; they are either too slow (iterative optimization algorithms) or not flexible to the encoding process (deep learning based end-to-end networks). In this paper, we develop fast and flexible algorithms for SCI based on the plug-and-play (PnP) framework. In addition to the widely used PnP-ADMM method, we further propose the PnP-GAP (generalized alternating projection) algorithm with a lower computational workload and prove the global convergence of PnP-GAP under the SCI hardware constraints. By employing deep denoising priors, we first time show that PnP can recover a UHD color video (3840x1644x48 with PNSR above 30dB) from a snapshot 2D measurement. Extensive results on both simulation and real datasets verify the superiority of our proposed algorithm. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yuan_Plug-and-Play_Algorithms_for_Large-Scale_Snapshot_Compressive_Imaging_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13654 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yuan_Plug-and-Play_Algorithms_for_Large-Scale_Snapshot_Compressive_Imaging_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yuan_Plug-and-Play_Algorithms_for_Large-Scale_Snapshot_Compressive_Imaging_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yuan_Plug-and-Play_Algorithms_for_CVPR_2020_supplemental.zip | null | null |
Single-Stage Semantic Segmentation From Image Labels | Nikita Araslanov, Stefan Roth | Recent years have seen a rapid growth in new approaches improving the accuracy of semantic segmentation in a weakly supervised setting, i.e. with only image-level labels available for training. However, this has come at the cost of increased model complexity and sophisticated multi-stage training procedures. This is in contrast to earlier work that used only a single stage -- training one segmentation network on image labels -- which was abandoned due to inferior segmentation accuracy. In this work, we first define three desirable properties of a weakly supervised method: local consistency, semantic fidelity, and completeness. Using these properties as guidelines, we then develop a segmentation-based network model and a self-supervised training scheme to train for semantic masks from image-level annotations in a single stage. We show that despite its simplicity, our method achieves results that are competitive with significantly more complex pipelines, substantially outperforming earlier single-stage methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Araslanov_Single-Stage_Semantic_Segmentation_From_Image_Labels_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.08104 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Araslanov_Single-Stage_Semantic_Segmentation_From_Image_Labels_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Araslanov_Single-Stage_Semantic_Segmentation_From_Image_Labels_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Araslanov_Single-Stage_Semantic_Segmentation_CVPR_2020_supplemental.pdf | null | null |
Spatially-Attentive Patch-Hierarchical Network for Adaptive Motion Deblurring | Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan | This paper tackles the problem of motion deblurring of dynamic scenes. Although end-to-end fully convolutional designs have recently advanced the state-of-the-art in non-uniform motion deblurring, their performance-complexity trade-off is still sub-optimal. Existing approaches achieve a large receptive field by increasing the number of generic convolution layers and kernel-size, but this comesat the expense of of the increase in model size and inference speed. In this work, we propose an efficient pixel adaptive and feature attentive design for handling large blur variations across different spatial locations and process each test image adaptively. We also propose an effective content-aware global-local filtering module that significantly improves performance by considering not only global dependencies but also by dynamically exploiting neighboring pixel information. We use a patch-hierarchical attentive architecture composed of the above module that implicitly discovers the spatial variations in the blur present in the input image and in turn, performs local and global modulation of intermediate features. Extensive qualitative and quantitative comparisons with prior art on deblurring benchmarks demonstrate that our design offers significant improvements over the state-of-the-art in accuracy as well as speed. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Suin_Spatially-Attentive_Patch-Hierarchical_Network_for_Adaptive_Motion_Deblurring_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.05343 | https://www.youtube.com/watch?v=MMqI6vrPB-4 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Suin_Spatially-Attentive_Patch-Hierarchical_Network_for_Adaptive_Motion_Deblurring_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Suin_Spatially-Attentive_Patch-Hierarchical_Network_for_Adaptive_Motion_Deblurring_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Front2Back: Single View 3D Shape Reconstruction via Front to Back Prediction | Yuan Yao, Nico Schertler, Enrique Rosales, Helge Rhodin, Leonid Sigal, Alla Sheffer | Reconstruction of a 3D shape from a single 2D image is a classical computer vision problem, whose difficulty stems from the inherent ambiguity of recovering occluded or only partially observed surfaces. Recent methods address this challenge through the use of largely unstructured neural networks that effectively distill conditional mapping and priors over 3D shape. In this work, we induce structure and geometric constraints by leveraging three core observations: (1) the surface of most everyday objects is often almost entirely exposed from pairs of typical opposite views; (2) everyday objects often exhibit global reflective symmetries which can be accurately predicted from single views; (3) opposite orthographic views of a 3D shape share consistent silhouettes. Following these observations, we first predict orthographic 2.5D visible surface maps (depth, normal and silhouette) from perspective 2D images, and detect global reflective symmetries in this data; second, we predict the back facing depth and normal maps using as input the front maps and, when available, the symmetric reflections of these maps; and finally, we reconstruct a 3D mesh from the union of these maps using a surface reconstruction method best suited for this data. Our experiments demonstrate that our framework outperforms state-of-the art approaches for 3D shape reconstructions from 2D and 2.5D data in terms of input fidelity and details preservation. Specifically, we achieve 12% better performance on average in ShapeNet benchmark dataset, and up to 19% for certain classes of objects (e.g., chairs and vessels). | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yao_Front2Back_Single_View_3D_Shape_Reconstruction_via_Front_to_Back_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.10589 | https://www.youtube.com/watch?v=yKcMD-4JQAg | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yao_Front2Back_Single_View_3D_Shape_Reconstruction_via_Front_to_Back_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yao_Front2Back_Single_View_3D_Shape_Reconstruction_via_Front_to_Back_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yao_Front2Back_Single_View_CVPR_2020_supplemental.pdf | null | null |
Reverse Perspective Network for Perspective-Aware Object Counting | Yifan Yang, Guorong Li, Zhe Wu, Li Su, Qingming Huang, Nicu Sebe | One of the critical challenges of object counting is the dramatic scale variations, which is introduced by arbitrary perspectives. We propose a reverse perspective network to solve the scale variations of input images, instead of generating perspective maps to smooth final outputs. The reverse perspective network explicitly evaluates the perspective distortions, and efficiently corrects the distortions by uniformly warping the input images. Then the proposed network delivers images with similar instance scales to the regressor. Thus the regression network doesn't need multi-scale receptive fields to match the various scales. Besides, to further solve the scale problem of more congested areas, we enhance the corresponding regions of ground-truth with the evaluation errors. Then we force the regressor to learn from the augmented ground-truth via an adversarial process. Furthermore, to verify the proposed model, we collected a vehicle counting dataset based on Unmanned Aerial Vehicles (UAVs). The proposed dataset has fierce scale variations. Extensive experimental results on four benchmark datasets show the improvements of our method against the state-of-the-arts. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_Reverse_Perspective_Network_for_Perspective-Aware_Object_Counting_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Reverse_Perspective_Network_for_Perspective-Aware_Object_Counting_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Reverse_Perspective_Network_for_Perspective-Aware_Object_Counting_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Show, Edit and Tell: A Framework for Editing Image Captions | Fawaz Sammani, Luke Melas-Kyriazi | Most image captioning frameworks generate captions directly from images, learning a mapping from visual features to natural language. However, editing existing captions can be easier than generating new ones from scratch. Intuitively, when editing captions, a model is not required to learn information that is already present in the caption (i.e. sentence structure), enabling it to focus on fixing details (e.g. replacing repetitive words). This paper proposes a novel approach to image captioning based on iterative adaptive refinement of an existing caption. Specifically, our caption-editing model consisting of two sub-modules: (1) EditNet, a language module with an adaptive copy mechanism (Copy-LSTM) and a Selective Copy Memory Attention mechanism (SCMA), and (2) DCNet, an LSTM-based denoising auto-encoder. These components enable our model to directly copy from and modify existing captions. Experiments demonstrate that our new approach achieves state of-art performance on the MS COCO dataset both with and without sequence-level training. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Sammani_Show_Edit_and_Tell_A_Framework_for_Editing_Image_Captions_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Sammani_Show_Edit_and_Tell_A_Framework_for_Editing_Image_Captions_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Sammani_Show_Edit_and_Tell_A_Framework_for_Editing_Image_Captions_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Sammani_Show_Edit_and_CVPR_2020_supplemental.pdf | null | null |
Self-Learning Video Rain Streak Removal: When Cyclic Consistency Meets Temporal Correspondence | Wenhan Yang, Robby T. Tan, Shiqi Wang, Jiaying Liu | In this paper, we address the problem of rain streaks removal in video by developing a self-learned rain streak removal method, which does not require any clean groundtruth images in the training process. The method is inspired by fact that the adjacent frames are highly correlated and can be regarded as different versions of identical scene, and rain streaks are randomly distributed along the temporal dimension. With this in mind, we construct a two-stage Self-Learned Deraining Network (SLDNet) to remove rain streaks based on both temporal correlation and consistency. In the first stage, SLDNet utilizes the temporal correlations and learns to predict the clean version of the current frame based on its adjacent rain video frames. In the second stage, SLDNet enforces the temporal consistency among different frames. It takes both the current rain frame and adjacent rain video frames to recover structural details. The first stage is responsible for reconstructing main structures, and the second stage is responsible for extracting structural details. We build our network architecture with two sub-tasks, i.e. motion estimation, and rain region detection, and optimize them jointly. Our extensive experiments demonstrate the effectiveness of our method, offering better results both quantitatively and qualitatively. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_Self-Learning_Video_Rain_Streak_Removal_When_Cyclic_Consistency_Meets_Temporal_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=l7ZDA3lJaJM | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Self-Learning_Video_Rain_Streak_Removal_When_Cyclic_Consistency_Meets_Temporal_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Self-Learning_Video_Rain_Streak_Removal_When_Cyclic_Consistency_Meets_Temporal_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yang_Self-Learning_Video_Rain_CVPR_2020_supplemental.pdf | null | null |
QEBA: Query-Efficient Boundary-Based Blackbox Attack | Huichen Li, Xiaojun Xu, Xiaolu Zhang, Shuang Yang, Bo Li | Machine learning (ML), especially deep neural networks (DNNs) have been widely used in various applications, including several safety-critical ones (e.g. autonomous driving). As a result, recent research about adversarial examples has raised great concerns. Such adversarial attacks can be achieved by adding a small magnitude of perturbation to the input to mislead model prediction. While several whitebox attacks have demonstrated their effectiveness, which assume that the attackers have full access to the machine learning models; blackbox attacks are more realistic in practice. In this paper, we propose a Query-Efficient Boundary-based blackbox Attack (QEBA) based only on model's final prediction labels. We theoretically show why previous boundary-based attack with gradient estimation on the whole gradient space is not efficient in terms of query numbers, and provide optimality analysis for our dimension reduction-based gradient estimation. On the other hand, we conducted extensive experiments on ImageNet and CelebA datasets to evaluate QEBA. We show that compared with the state-of-the-art blackbox attacks, QEBA is able to use a smaller number of queries to achieve a lower magnitude of perturbation with 100% attack success rate. We also show case studies of attacks on real-world APIs including MEGVII Face++ and Microsoft Azure. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_QEBA_Query-Efficient_Boundary-Based_Blackbox_Attack_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.14137 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_QEBA_Query-Efficient_Boundary-Based_Blackbox_Attack_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_QEBA_Query-Efficient_Boundary-Based_Blackbox_Attack_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_QEBA_Query-Efficient_Boundary-Based_CVPR_2020_supplemental.pdf | null | null |
Generating and Exploiting Probabilistic Monocular Depth Estimates | Zhihao Xia, Patrick Sullivan, Ayan Chakrabarti | Beyond depth estimation from a single image, the monocular cue is useful in a broader range of depth inference applications and settings---such as when one can leverage other available depth cues for improved accuracy. Currently, different applications, with different inference tasks and combinations of depth cues, are solved via different specialized networks---trained separately for each application. Instead, we propose a versatile task-agnostic monocular model that outputs a probability distribution over scene depth given an input color image, as a sample approximation of outputs from a patch-wise conditional VAE. We show that this distributional output can be used to enable a variety of inference tasks in different settings, without needing to retrain for each application. Across a diverse set of applications (depth completion, user guided estimation, etc.), our common model yields results with high accuracy---comparable to or surpassing that of state-of-the-art methods dependent on application-specific networks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xia_Generating_and_Exploiting_Probabilistic_Monocular_Depth_Estimates_CVPR_2020_paper.pdf | http://arxiv.org/abs/1906.05739 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xia_Generating_and_Exploiting_Probabilistic_Monocular_Depth_Estimates_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xia_Generating_and_Exploiting_Probabilistic_Monocular_Depth_Estimates_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Xia_Generating_and_Exploiting_CVPR_2020_supplemental.pdf | null | null |
Context-Aware Attention Network for Image-Text Retrieval | Qi Zhang, Zhen Lei, Zhaoxiang Zhang, Stan Z. Li | As a typical cross-modal problem, image-text bi-directional retrieval relies heavily on the joint embedding learning and similarity measure for each image-text pair. It remains challenging because prior works seldom explore semantic correspondences between modalities and semantic correlations in a single modality at the same time. In this work, we propose a unified Context-Aware Attention Network (CAAN), which selectively focuses on critical local fragments (regions and words) by aggregating the global context. Specifically, it simultaneously utilizes global inter-modal alignments and intra-modal correlations to discover latent semantic relations. Considering the interactions between images and sentences in the retrieval process, intra-modal correlations are derived from the second-order attention of region-word alignments instead of intuitively comparing the distance between original features. Our method achieves fairly competitive results on two generic image-text retrieval datasets Flickr30K and MS-COCO. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Context-Aware_Attention_Network_for_Image-Text_Retrieval_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Context-Aware_Attention_Network_for_Image-Text_Retrieval_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Context-Aware_Attention_Network_for_Image-Text_Retrieval_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
From Image Collections to Point Clouds With Self-Supervised Shape and Pose Networks | K L Navaneet, Ansu Mathew, Shashank Kashyap, Wei-Chih Hung, Varun Jampani, R. Venkatesh Babu | Reconstructing 3D models from 2D images is one of the fundamental problems in computer vision. In this work, we propose a deep learning technique for 3D object reconstruction from a single image. Contrary to recent works that either use 3D supervision or multi-view supervision, we use only single view images with no pose information during training as well. This makes our approach more practical requiring only an image collection of an object category and the corresponding silhouettes. We learn both 3D point cloud reconstruction and pose estimation networks in a self-supervised manner, making use of differentiable point cloud renderer to train with 2D supervision. A key novelty of the proposed technique is to impose 3D geometric reasoning into predicted 3D point clouds by rotating them with randomly sampled poses and then enforcing cycle consistency on both 3D reconstructions and poses. In addition, using single-view supervision allows us to do test-time optimization on a given test image. Experiments on the synthetic ShapeNet and real-world Pix3D datasets demonstrate that our approach, despite using less supervision, can achieve competitive performance compared to pose-supervised and multi-view supervised approaches. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Navaneet_From_Image_Collections_to_Point_Clouds_With_Self-Supervised_Shape_and_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.01939 | https://www.youtube.com/watch?v=IbJseAnyivY | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Navaneet_From_Image_Collections_to_Point_Clouds_With_Self-Supervised_Shape_and_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Navaneet_From_Image_Collections_to_Point_Clouds_With_Self-Supervised_Shape_and_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Navaneet_From_Image_Collections_CVPR_2020_supplemental.pdf | null | null |
Predicting Goal-Directed Human Attention Using Inverse Reinforcement Learning | Zhibo Yang, Lihan Huang, Yupei Chen, Zijun Wei, Seoyoung Ahn, Gregory Zelinsky, Dimitris Samaras, Minh Hoai | Human gaze behavior prediction is important for behavioral vision and for computer vision applications. Most models mainly focus on predicting free-viewing behavior using saliency maps, but do not generalize to goal-directed behavior, such as when a person searches for a visual target object. We propose the first inverse reinforcement learning (IRL) model to learn the internal reward function and policy used by humans during visual search. We modeled the viewer's internal belief states as dynamic contextual belief maps of object locations. These maps were learned and then used to predict behavioral scanpaths for multiple target categories. To train and evaluate our IRL model we created COCO-Search18, which is now the largest dataset of high-quality search fixations in existence. COCO-Search18 has 10 participants searching for each of 18 target-object categories in 6202 images, making about 300,000 goal-directed fixations. When trained and evaluated on COCO-Search18, the IRL model outperformed baseline models in predicting search fixation scanpaths, both in terms of similarity to human search behavior and search efficiency. Finally, reward maps recovered by the IRL model reveal distinctive target-dependent patterns of object prioritization, which we interpret as a learned object context. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_Predicting_Goal-Directed_Human_Attention_Using_Inverse_Reinforcement_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.14310 | https://www.youtube.com/watch?v=Oa2GKJh4zbA | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Predicting_Goal-Directed_Human_Attention_Using_Inverse_Reinforcement_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Predicting_Goal-Directed_Human_Attention_Using_Inverse_Reinforcement_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yang_Predicting_Goal-Directed_Human_CVPR_2020_supplemental.pdf | null | null |
gDLS*: Generalized Pose-and-Scale Estimation Given Scale and Gravity Priors | Victor Fragoso, Joseph DeGol, Gang Hua | Many real-world applications in augmented reality (AR), 3D mapping, and robotics require both fast and accurate estimation of camera poses and scales from multiple images captured by multiple cameras or a single moving camera. Achieving high speed and maintaining high accuracy in a pose-and-scale estimator are often conflicting goals. To simultaneously achieve both, we exploit a priori knowledge about the solution space. We present gDLS*, a generalized-camera-model pose-and-scale estimator that utilizes rotation and scale priors. gDLS* allows an application to flexibly weigh the contribution of each prior, which is important since priors often come from noisy sensors. Compared to state-of-the-art generalized-pose-and-scale estimators (e.g., gDLS), our experiments on both synthetic and real data consistently demonstrate that gDLS* accelerates the estimation process and improves scale and pose accuracy. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Fragoso_gDLS_Generalized_Pose-and-Scale_Estimation_Given_Scale_and_Gravity_Priors_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.02052 | https://www.youtube.com/watch?v=ckfou0UKAQA | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Fragoso_gDLS_Generalized_Pose-and-Scale_Estimation_Given_Scale_and_Gravity_Priors_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Fragoso_gDLS_Generalized_Pose-and-Scale_Estimation_Given_Scale_and_Gravity_Priors_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Fragoso_gDLS_Generalized_Pose-and-Scale_CVPR_2020_supplemental.pdf | null | null |
Perceptual Quality Assessment of Smartphone Photography | Yuming Fang, Hanwei Zhu, Yan Zeng, Kede Ma, Zhou Wang | As smartphones become people's primary cameras to take photos, the quality of their cameras and the associated computational photography modules has become a de facto standard in evaluating and ranking smartphones in the consumer market. We conduct so far the most comprehensive study of perceptual quality assessment of smartphone photography. We introduce the Smartphone Photography Attribute and Quality (SPAQ) database, consisting of 11,125 pictures taken by 66 smartphones, where each image is attached with so far the richest annotations. Specifically, we collect a series of human opinions for each image, including image quality, image attributes (brightness, colorfulness, contrast, noisiness, and sharpness), and scene category labels (animal, cityscape, human, indoor scene, landscape, night scene, plant, still life, and others) in a well-controlled laboratory environment. The exchangeable image file format (EXIF) data for all images are also recorded to aid deeper analysis. We also make the first attempts using the database to train blind image quality assessment (BIQA) models constructed by baseline and multi-task deep neural networks. The results provide useful insights on how EXIF data, image attributes and high-level semantics interact with image quality, how next-generation BIQA models can be designed, and how better computational photography systems can be optimized on mobile devices. The database along with the proposed BIQA models are available at https://github.com/h4nwei/SPAQ. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Fang_Perceptual_Quality_Assessment_of_Smartphone_Photography_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Fang_Perceptual_Quality_Assessment_of_Smartphone_Photography_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Fang_Perceptual_Quality_Assessment_of_Smartphone_Photography_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Fang_Perceptual_Quality_Assessment_CVPR_2020_supplemental.pdf | null | null |
When NAS Meets Robustness: In Search of Robust Architectures Against Adversarial Attacks | Minghao Guo, Yuzhe Yang, Rui Xu, Ziwei Liu, Dahua Lin | Recent advances in adversarial attacks uncover the intrinsic vulnerability of modern deep neural networks. Since then, extensive efforts have been devoted to enhancing the robustness of deep networks via specialized learning algorithms and loss functions. In this work, we take an architectural perspective and investigate the patterns of network architectures that are resilient to adversarial attacks. To obtain the large number of networks needed for this study, we adopt one-shot neural architecture search, training a large network for once and then finetuning the sub-networks sampled therefrom. The sampled architectures together with the accuracies they achieve provide a rich basis for our study. Our "robust architecture Odyssey" reveals several valuable observations: 1) densely connected patterns result in improved robustness; 2) under computational budget, adding convolution operations to direct connection edge is effective; 3) flow of solution procedure (FSP) matrix is a good indicator of network robustness. Based on these observations, we discover a family of robust architectures (RobNets). On various datasets, including CIFAR, SVHN, Tiny-ImageNet, and ImageNet, RobNets exhibit superior robustness performance to other widely used architectures. Notably, RobNets substantially improve the robust accuracy ( 5% absolute gains) under both white-box and black-box attacks, even with fewer parameter numbers. Code is available at https://github.com/gmh14/RobNets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Guo_When_NAS_Meets_Robustness_In_Search_of_Robust_Architectures_Against_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.10695 | https://www.youtube.com/watch?v=3qwWzuFDcsA | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_When_NAS_Meets_Robustness_In_Search_of_Robust_Architectures_Against_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_When_NAS_Meets_Robustness_In_Search_of_Robust_Architectures_Against_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Guo_When_NAS_Meets_CVPR_2020_supplemental.pdf | null | null |
Fast Symmetric Diffeomorphic Image Registration with Convolutional Neural Networks | Tony C.W. Mok, Albert C.S. Chung | Diffeomorphic deformable image registration is crucial in many medical image studies, as it offers unique, special features including topology preservation and invertibility of the transformation. Recent deep learning-based deformable image registration methods achieve fast image registration by leveraging a convolutional neural network (CNN) to learn the spatial transformation from the synthetic ground truth or the similarity metric. However, these approaches often ignore the topology preservation of the transformation and the smoothness of the transformation which is enforced by a global smoothing energy function alone. Moreover, deep learning-based approaches often estimate the displacement field directly, which cannot guarantee the existence of the inverse transformation. In this paper, we present a novel, efficient unsupervised symmetric image registration method which maximizes the similarity between images within the space of diffeomorphic maps and estimates both forward and inverse transformations simultaneously. We evaluate our method on 3D image registration with a large scale brain image dataset. Our method achieves state-of-the-art registration accuracy and running time while maintaining desirable diffeomorphic properties. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Mok_Fast_Symmetric_Diffeomorphic_Image_Registration_with_Convolutional_Neural_Networks_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.09514 | https://www.youtube.com/watch?v=Z9xXZFsph5w | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Mok_Fast_Symmetric_Diffeomorphic_Image_Registration_with_Convolutional_Neural_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Mok_Fast_Symmetric_Diffeomorphic_Image_Registration_with_Convolutional_Neural_Networks_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Deep White-Balance Editing | Mahmoud Afifi, Michael S. Brown | We introduce a deep learning approach to realistically edit an sRGB image's white balance. Cameras capture sensor images that are rendered by their integrated signal processor (ISP) to a standard RGB (sRGB) color space encoding. The ISP rendering begins with a white-balance procedure that is used to remove the color cast of the scene's illumination. The ISP then applies a series of nonlinear color manipulations to enhance the visual quality of the final sRGB image. Recent work by [3] showed that sRGB images that were rendered with the incorrect white balance cannot be easily corrected due to the ISP's nonlinear rendering. The work in [3] proposed a k-nearest neighbor (KNN) solution based on tens of thousands of image pairs. We propose to solve this problem with a deep neural network (DNN) architecture trained in an end-to-end manner to learn the correct white balance. Our DNN maps an input image to two additional white-balance settings corresponding to indoor and outdoor illuminations. Our solution not only is more accurate than the KNN approach in terms of correcting a wrong white-balance setting but also provides the user the freedom to edit the white balance in the sRGB image to other illumination settings. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Afifi_Deep_White-Balance_Editing_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.01354 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Afifi_Deep_White-Balance_Editing_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Afifi_Deep_White-Balance_Editing_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Afifi_Deep_White-Balance_Editing_CVPR_2020_supplemental.pdf | null | null |
Convolution in the Cloud: Learning Deformable Kernels in 3D Graph Convolution Networks for Point Cloud Analysis | Zhi-Hao Lin, Sheng-Yu Huang, Yu-Chiang Frank Wang | Point clouds are among the popular geometry representations for 3D vision applications. However, without regular structures like 2D images, processing and summarizing information over these unordered data points are very challenging. Although a number of previous works attempt to analyze point clouds and achieve promising performances, their performances would degrade significantly when data variations like shift and scale changes are presented. In this paper, we propose 3D Graph Convolution Networks (3D-GCN), which is designed to extract local 3D features from point clouds across scales, while shift and scale-invariance properties are introduced. The novelty of our 3D-GCN lies in the definition of learnable kernels with a graph max-pooling mechanism. We show that 3D-GCN can be applied to 3D classification and segmentation tasks, with ablation studies and visualizations verifying the design of 3D-GCN. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lin_Convolution_in_the_Cloud_Learning_Deformable_Kernels_in_3D_Graph_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=olCVC3ayI24 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Convolution_in_the_Cloud_Learning_Deformable_Kernels_in_3D_Graph_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Convolution_in_the_Cloud_Learning_Deformable_Kernels_in_3D_Graph_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lin_Convolution_in_the_CVPR_2020_supplemental.pdf | null | null |
Upgrading Optical Flow to 3D Scene Flow Through Optical Expansion | Gengshan Yang, Deva Ramanan | We describe an approach for upgrading 2D optical flow to 3D scene flow. Our key insight is that dense optical expansion - which can be reliably inferred from monocular frame pairs - reveals changes in depth of scene elements, e.g., things moving closer will get bigger. When integrated with camera intrinsics, optical expansion can be converted into a normalized 3D scene flow vectors that provide meaningful directions of 3D movement, but not their magnitude (due to an underlying scale ambiguity). Normalized scene flow can be further "upgraded" to the true 3D scene flow knowing depth in one frame. We show that dense optical expansion between two views can be learned from annotated optical flow maps or unlabeled video sequences, and applied to a variety of dynamic 3D perception tasks including optical scene flow, LiDAR scene flow, time-to-collision estimation and depth estimation, often demonstrating significant improvement over the prior art. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_Upgrading_Optical_Flow_to_3D_Scene_Flow_Through_Optical_Expansion_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Upgrading_Optical_Flow_to_3D_Scene_Flow_Through_Optical_Expansion_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Upgrading_Optical_Flow_to_3D_Scene_Flow_Through_Optical_Expansion_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yang_Upgrading_Optical_Flow_CVPR_2020_supplemental.pdf | null | null |
Collaborative Distillation for Ultra-Resolution Universal Style Transfer | Huan Wang, Yijun Li, Yuehai Wang, Haoji Hu, Ming-Hsuan Yang | Universal style transfer methods typically leverage rich representations from deep Convolutional Neural Network (CNN) models (e.g., VGG-19) pre-trained on large collections of images. Despite the effectiveness, its application is heavily constrained by the large model size to handle ultra-resolution images given limited memory. In this work, we present a new knowledge distillation method (named Collaborative Distillation) for encoder-decoder based neural style transfer to reduce the convolutional filters. The main idea is underpinned by a finding that the encoder-decoder pairs construct an exclusive collaborative relationship, which is regarded as a new kind of knowledge for style transfer models. Moreover, to overcome the feature size mismatch when applying collaborative distillation, a linear embedding loss is introduced to drive the student network to learn a linear embedding of the teacher's features. Extensive experiments show the effectiveness of our method when applied to different universal style transfer approaches (WCT and AdaIN), even if the model size is reduced by 15.5 times. Especially, on WCT with the compressed models, we achieve ultra-resolution (over 40 megapixels) universal style transfer on a 12GB GPU for the first time. Further experiments on optimization-based stylization scheme show the generality of our algorithm on different stylization paradigms. Our code and trained models are available at https://github.com/mingsun-tse/collaborative-distillation. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Collaborative_Distillation_for_Ultra-Resolution_Universal_Style_Transfer_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.08436 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Collaborative_Distillation_for_Ultra-Resolution_Universal_Style_Transfer_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Collaborative_Distillation_for_Ultra-Resolution_Universal_Style_Transfer_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
L2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks | Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen | Graph convolution networks (GCN) are increasingly popular in many applications, yet remain notoriously hard to train over large graph datasets. They need to compute node representations recursively from their neighbors. Current GCN training algorithms suffer from either high computational costs that grow exponentially with the number of layers, or high memory usage for loading the entire graph and node embeddings. In this paper, we propose a novel efficient layer-wise training framework for GCN (L-GCN), that disentangles feature aggregation and feature transformation during training, hence greatly reducing time and memory complexities. We present theoretical analysis for L-GCN under the graph isomorphism framework, that L-GCN leads to as powerful GCNs as the more costly conventional training algorithm does, under mild conditions. We further propose L^2-GCN, which learns a controller for each layer that can automatically adjust the training epochs per layer in L-GCN. Experiments show that L-GCN is faster than state-of-the-arts by at least an order of magnitude, with a consistent of memory usage not dependent on dataset size, while maintaining comparable prediction performance. With the learned controller, L^2-GCN can further cut the training time in half. Our codes are available at https://github.com/Shen-Lab/L2-GCN. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/You_L2-GCN_Layer-Wise_and_Learned_Efficient_Training_of_Graph_Convolutional_Networks_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=ptwGoq0C1RM | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/You_L2-GCN_Layer-Wise_and_Learned_Efficient_Training_of_Graph_Convolutional_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/You_L2-GCN_Layer-Wise_and_Learned_Efficient_Training_of_Graph_Convolutional_Networks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/You_L2-GCN_Layer-Wise_and_CVPR_2020_supplemental.pdf | null | null |
Mapillary Street-Level Sequences: A Dataset for Lifelong Place Recognition | Frederik Warburg, Soren Hauberg, Manuel Lopez-Antequera, Pau Gargallo, Yubin Kuang, Javier Civera | Lifelong place recognition is an essential and challenging task in computer vision with vast applications in robust localization and efficient large-scale 3D reconstruction. Progress is currently hindered by a lack of large, diverse, publicly available datasets. We contribute with Mapillary Street-Level Sequences (SLS), a large dataset for urban and suburban place recognition from image sequences. It contains more than 1.6 million images curated from the Mapillary collaborative mapping platform. The dataset is orders of magnitude larger than current data sources, and is designed to reflect the diversities of true lifelong learning. It features images from 30 major cities across six continents, hundreds of distinct cameras, and substantially different viewpoints and capture times, spanning all seasons over a nine year period. All images are geo-located with GPS and compass, and feature high-level attributes such as road type. We propose a set of benchmark tasks designed to push state-of-the-art performance and provide baseline studies. We show that current state-of-the-art methods still have a long way to go, and that the lack of diversity in existing datasets have prevented generalization to new environments. The dataset and benchmarks are available for academic research. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Warburg_Mapillary_Street-Level_Sequences_A_Dataset_for_Lifelong_Place_Recognition_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Warburg_Mapillary_Street-Level_Sequences_A_Dataset_for_Lifelong_Place_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Warburg_Mapillary_Street-Level_Sequences_A_Dataset_for_Lifelong_Place_Recognition_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Warburg_Mapillary_Street-Level_Sequences_CVPR_2020_supplemental.pdf | null | null |
M-LVC: Multiple Frames Prediction for Learned Video Compression | Jianping Lin, Dong Liu, Houqiang Li, Feng Wu | We propose an end-to-end learned video compression scheme for low-latency scenarios. Previous methods are limited in using the previous one frame as reference. Our method introduces the usage of the previous multiple frames as references. In our scheme, the motion vector (MV) field is calculated between the current frame and the previous one. With multiple reference frames and associated multiple MV fields, our designed network can generate more accurate prediction of the current frame, yielding less residual. Multiple reference frames also help generate MV prediction, which reduces the coding cost of MV field. We use two deep auto-encoders to compress the residual and the MV, respectively. To compensate for the compression error of the auto-encoders, we further design a MV refinement network and a residual refinement network, taking use of the multiple reference frames as well. All the modules in our scheme are jointly optimized through a single rate-distortion loss function. We use a step-by-step training strategy to optimize the entire scheme. Experimental results show that the proposed method outperforms the existing learned video compression methods for low-latency mode. Our method also performs better than H.265 in both PSNR and MS-SSIM. Our code and models are publicly available. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lin_M-LVC_Multiple_Frames_Prediction_for_Learned_Video_Compression_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_M-LVC_Multiple_Frames_Prediction_for_Learned_Video_Compression_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_M-LVC_Multiple_Frames_Prediction_for_Learned_Video_Compression_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lin_M-LVC_Multiple_Frames_CVPR_2020_supplemental.pdf | null | null |
Fusing Wearable IMUs With Multi-View Images for Human Pose Estimation: A Geometric Approach | Zhe Zhang, Chunyu Wang, Wenhu Qin, Wenjun Zeng | We propose to estimate 3D human pose from multi-view images and a few IMUs attached at person's limbs. It operates by firstly detecting 2D poses from the two signals, and then lifting them to the 3D space. We present a geometric approach to reinforce the visual features of each pair of joints based on the IMUs. This notably improves 2D pose estimation accuracy especially when one joint is occluded. We call this approach Orientation Regularized Network (ORN). Then we lift the multi-view 2D poses to the 3D space by an Orientation Regularized Pictorial Structure Model (ORPSM) which jointly minimizes the projection error between the 3D and 2D poses, along with the discrepancy between the 3D pose and IMU orientations. The simple two-step approach reduces the error of the state-of-the-art by a large margin on a public dataset. Our code will be released at https://github.com/microsoft/imu-human-pose-estimation-pytorch. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Fusing_Wearable_IMUs_With_Multi-View_Images_for_Human_Pose_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.11163 | https://www.youtube.com/watch?v=fyhGgB7XS84 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Fusing_Wearable_IMUs_With_Multi-View_Images_for_Human_Pose_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Fusing_Wearable_IMUs_With_Multi-View_Images_for_Human_Pose_Estimation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhang_Fusing_Wearable_IMUs_CVPR_2020_supplemental.pdf | null | null |
DNU: Deep Non-Local Unrolling for Computational Spectral Imaging | Lizhi Wang, Chen Sun, Maoqing Zhang, Ying Fu, Hua Huang | Computational spectral imaging has been striving to capture the spectral information of the dynamic world in the last few decades. In this paper, we propose an interpretable neural network for computational spectral imaging. First, we introduce a novel data-driven prior that can adaptively exploit both the local and non-local correlations among the spectral image. Our data-driven prior is integrated as a regularizer into the reconstruction problem. Then, we propose to unroll the reconstruction problem into an optimization-inspired deep neural network. The architecture of the network has high interpretability by explicitly characterizing the image correlation and the system imaging model. Finally, we learn the complete parameters in the network through end-to-end training, enabling robust performance with high spatial-spectral fidelity. Extensive simulation and hardware experiments validate the superior performance of our method over state-of-the-art methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_DNU_Deep_Non-Local_Unrolling_for_Computational_Spectral_Imaging_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_DNU_Deep_Non-Local_Unrolling_for_Computational_Spectral_Imaging_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_DNU_Deep_Non-Local_Unrolling_for_Computational_Spectral_Imaging_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Single-Stage 6D Object Pose Estimation | Yinlin Hu, Pascal Fua, Wei Wang, Mathieu Salzmann | Most recent 6D pose estimation frameworks first rely on a deep network to establish correspondences between 3D object keypoints and 2D image locations and then use a variant of a RANSAC-based Perspective-n-Point (PnP) algorithm. This two-stage process, however, is suboptimal: First, it is not end-to-end trainable. Second, training the deep network relies on a surrogate loss that does not directly reflect the final 6D pose estimation task. In this work, we introduce a deep architecture that directly regresses 6D poses from correspondences. It takes as input a group of candidate correspondences for each 3D keypoint and accounts for the fact that the order of the correspondences within each group is irrelevant, while the order of the groups, that is, of the 3D keypoints, is fixed. Our architecture is generic and can thus be exploited in conjunction with existing correspondence-extraction networks so as to yield single-stage 6D pose estimation frameworks. Our experiments demonstrate that these single-stage frameworks consistently outperform their two-stage counterparts in terms of both accuracy and speed. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hu_Single-Stage_6D_Object_Pose_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.08324 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hu_Single-Stage_6D_Object_Pose_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hu_Single-Stage_6D_Object_Pose_Estimation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
A Physics-Based Noise Formation Model for Extreme Low-Light Raw Denoising | Kaixuan Wei, Ying Fu, Jiaolong Yang, Hua Huang | Lacking rich and realistic data, learned single image denoising algorithms generalize poorly in real raw images that not resemble the data used for training. Although the problem can be alleviated by the heteroscedastic Gaussian noise model, the noise sources caused by digital camera electronics are still largely overlooked, despite their significant effect on raw measurement, especially under extremely low-light condition. To address this issue, we present a highly accurate noise formation model based on the characteristics of CMOS photosensors, thereby enabling us to synthesize realistic samples that better match the physics of image formation process. Given the proposed noise model, we additionally propose a method to calibrate the noise parameters for available modern digital cameras, which is simple and reproducible for any new device. We systematically study the generalizability of a neural network trained with existing schemes, by introducing a new low-light denoising dataset that covers many modern digital cameras from diverse brands. Extensive empirical results collectively show that by utilizing our proposed noise formation model, a network can reach the capability as if it had been trained with rich real data, which demonstrates the effectiveness of our noise formation model. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wei_A_Physics-Based_Noise_Formation_Model_for_Extreme_Low-Light_Raw_Denoising_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12751 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wei_A_Physics-Based_Noise_Formation_Model_for_Extreme_Low-Light_Raw_Denoising_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wei_A_Physics-Based_Noise_Formation_Model_for_Extreme_Low-Light_Raw_Denoising_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
AdaBits: Neural Network Quantization With Adaptive Bit-Widths | Qing Jin, Linjie Yang, Zhenyu Liao | Deep neural networks with adaptive configurations have gained increasing attention due to the instant and flexible deployment of these models on platforms with different resource budgets. In this paper, we investigate a novel option to achieve this goal by enabling adaptive bit-widths of weights and activations in the model. We first examine the benefits and challenges of training quantized model with adaptive bit-widths, and then experiment with several approaches including direct adaptation, progressive training and joint training. We discover that joint training is able to produce comparable performance on the adaptive model as individual models. We also propose a new technique named Switchable Clipping Level (S-CL) to further improve quantized models at the lowest bit-width. With our proposed techniques applied on a bunch of models including MobileNet V1/V2 and ResNet50, we demonstrate that bit-width of weights and activations is a new option for adaptively executable deep neural networks, offering a distinct opportunity for improved accuracy-efficiency trade-off as well as instant adaptation according to the platform constraints in real-world applications. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jin_AdaBits_Neural_Network_Quantization_With_Adaptive_Bit-Widths_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.09666 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jin_AdaBits_Neural_Network_Quantization_With_Adaptive_Bit-Widths_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jin_AdaBits_Neural_Network_Quantization_With_Adaptive_Bit-Widths_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Jin_AdaBits_Neural_Network_CVPR_2020_supplemental.pdf | null | null |
A Lighting-Invariant Point Processor for Shading | Kathryn Heal, Jialiang Wang, Steven J. Gortler, Todd Zickler | Under the conventional diffuse shading model with unknown directional lighting, the set of quadratic surface shapes that are consistent with the spatial derivatives of intensity at a single image point is a two-dimensional algebraic variety embedded in the five-dimensional space of quadratic shapes. We describe the geometry of this variety, and we introduce a concise feedforward model that computes an explicit, differentiable approximation of the variety from the intensity and its derivatives at any single image point. The result is a parallelizable processor that operates at each image point and produces a lighting-invariant descriptor of the continuous set of compatible surface shapes at the point. We describe two applications of this processor: two-shot uncalibrated photometric stereo and quadratic-surface shape from shading. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Heal_A_Lighting-Invariant_Point_Processor_for_Shading_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Heal_A_Lighting-Invariant_Point_Processor_for_Shading_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Heal_A_Lighting-Invariant_Point_Processor_for_Shading_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Heal_A_Lighting-Invariant_Point_CVPR_2020_supplemental.pdf | null | null |
Learning a Reinforced Agent for Flexible Exposure Bracketing Selection | Zhouxia Wang, Jiawei Zhang, Mude Lin, Jiong Wang, Ping Luo, Jimmy Ren | Automatically selecting exposure bracketing (images exposed differently) is important to obtain a high dynamic range image by using multi-exposure fusion. Unlike previous methods that have many restrictions such as requiring camera response function, sensor noise model, and a stream of preview images with different exposures (not accessible in some scenarios e.g. mobile applications), we propose a novel deep neural network to automatically select exposure bracketing, named EBSNet, which is sufficiently flexible without having the above restrictions. EBSNet is formulated as a reinforced agent that is trained by maximizing rewards provided by a multi-exposure fusion network (MEFNet). By utilizing the illumination and semantic information extracted from just a single auto-exposure preview image, EBSNet enables to select an optimal exposure bracketing for multi-exposure fusion. EBSNet and MEFNet can be jointly trained to produce favorable results against recent state-of-the-art approaches. To facilitate future research, we provide a new benchmark dataset for multi-exposure selection and fusion. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Learning_a_Reinforced_Agent_for_Flexible_Exposure_Bracketing_Selection_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.12536 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Learning_a_Reinforced_Agent_for_Flexible_Exposure_Bracketing_Selection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Learning_a_Reinforced_Agent_for_Flexible_Exposure_Bracketing_Selection_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_Learning_a_Reinforced_CVPR_2020_supplemental.pdf | null | null |
Focus on Defocus: Bridging the Synthetic to Real Domain Gap for Depth Estimation | Maxim Maximov, Kevin Galim, Laura Leal-Taixe | Data-driven depth estimation methods struggle with the generalization outside their training scenes due to the immense variability of the real-world scenes. This problem can be partially addressed by utilising synthetically generated images, but closing the synthetic-real domain gap is far from trivial. In this paper, we tackle this issue by using domain invariant defocus blur as direct supervision. We leverage defocus cues by using a permutation invariant convolutional neural network that encourages the network to learn from the differences between images with a different point of focus. Our proposed network uses the defocus map as an intermediate supervisory signal. We are able to train our model completely on synthetic data and directly apply it to a wide range of real-world images. We evaluate our model on synthetic and real datasets, showing compelling generalization results and state-of-the-art depth prediction. The dataset and code are available at https://github.com/dvl-tum/defocus-net. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Maximov_Focus_on_Defocus_Bridging_the_Synthetic_to_Real_Domain_Gap_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=rCeThpcyMXU | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Maximov_Focus_on_Defocus_Bridging_the_Synthetic_to_Real_Domain_Gap_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Maximov_Focus_on_Defocus_Bridging_the_Synthetic_to_Real_Domain_Gap_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Maximov_Focus_on_Defocus_CVPR_2020_supplemental.pdf | null | null |
Defending Against Universal Attacks Through Selective Feature Regeneration | Tejas Borkar, Felix Heide, Lina Karam | Deep neural network (DNN) predictions have been shown to be vulnerable to carefully crafted adversarial perturbations. Specifically, image-agnostic (universal adversarial) perturbations added to any image can fool a target network into making erroneous predictions. Departing from existing defense strategies that work mostly in the image domain, we present a novel defense which operates in the DNN feature domain and effectively defends against such universal perturbations. Our approach identifies pre-trained convolutional features that are most vulnerable to adversarial noise and deploys trainable feature regeneration units which transform these DNN filter activations into resilient features that are robust to universal perturbations. Regenerating only the top 50% adversarially susceptible activations in at most 6 DNN layers and leaving all remaining DNN activations unchanged, we outperform existing defense strategies across different network architectures by more than 10% in restored accuracy. We show that without any additional modification, our defense trained on ImageNet with one type of universal attack examples effectively defends against other types of unseen universal attacks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Borkar_Defending_Against_Universal_Attacks_Through_Selective_Feature_Regeneration_CVPR_2020_paper.pdf | http://arxiv.org/abs/1906.03444 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Borkar_Defending_Against_Universal_Attacks_Through_Selective_Feature_Regeneration_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Borkar_Defending_Against_Universal_Attacks_Through_Selective_Feature_Regeneration_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Borkar_Defending_Against_Universal_CVPR_2020_supplemental.pdf | null | null |
Cross-View Correspondence Reasoning Based on Bipartite Graph Convolutional Network for Mammogram Mass Detection | Yuhang Liu, Fandong Zhang, Qianyi Zhang, Siwen Wang, Yizhou Wang, Yizhou Yu | Mammogram mass detection is of great clinical significance due to its high proportion in breast cancers. The information from cross views (i.e., mediolateral oblique and cranio-caudal) is highly related and complementary, and is helpful to make comprehensive decisions. However, unlike radiologists who are able to recognize masses with reasoning ability in cross-view images, most existing methods lack the ability to reason under the guidance of domain knowledge, thus it limits the performance. In this paper, we introduce bipartite graph convolutional network to endow existing methods with cross-view reasoning ability of radiologists in mammogram mass detection. The bipartite node sets are constructed by cross-view images respectively to represent relatively consistent regions in breasts, while the bipartite edge learns to model both inherent cross-view geometric constraints and appearance similarities between correspondences. Based on the bipartite graph, the information propagates methodically through correspondences and enables spatial visual features equipped with customized cross-view reasoning ability. Experimental results on DDSM dataset demonstrate that the proposed algorithm achieves state-of-the-art performance. Besides, visual analysis shows the model has a clear physical meaning, which is helpful for radiologists in clinical interpretation. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Cross-View_Correspondence_Reasoning_Based_on_Bipartite_Graph_Convolutional_Network_for_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=9CWG4FWGXuo | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Cross-View_Correspondence_Reasoning_Based_on_Bipartite_Graph_Convolutional_Network_for_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Cross-View_Correspondence_Reasoning_Based_on_Bipartite_Graph_Convolutional_Network_for_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Liu_Cross-View_Correspondence_Reasoning_CVPR_2020_supplemental.pdf | null | null |
Image Search With Text Feedback by Visiolinguistic Attention Learning | Yanbei Chen, Shaogang Gong, Loris Bazzani | Image search with text feedback has promising impacts in various real-world applications, such as e-commerce and internet search. Given a reference image and text feedback from user, the goal is to retrieve images that not only resemble the input image, but also change certain aspects in accordance with the given text. This is a challenging task as it requires the synergistic understanding of both image and text. In this work, we tackle this task by a novel Visiolinguistic Attention Learning (VAL) framework. Specifically, we propose a composite transformer that can be seamlessly plugged in a CNN to selectively preserve and transform the visual features conditioned on language semantics. By inserting multiple composite transformers at varying depths, VAL is incentive to encapsulate the multi-granular visiolinguistic information, thus yielding an expressive representation for effective image search. We conduct comprehensive evaluation on three datasets: Fashion200k, Shoes and FashionIQ. Extensive experiments show our model exceeds existing approaches on all datasets, demonstrating consistent superiority in coping with various text feedbacks, including attribute-like and natural language descriptions. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Image_Search_With_Text_Feedback_by_Visiolinguistic_Attention_Learning_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Image_Search_With_Text_Feedback_by_Visiolinguistic_Attention_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Image_Search_With_Text_Feedback_by_Visiolinguistic_Attention_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_Image_Search_With_CVPR_2020_supplemental.pdf | null | null |
Joint 3D Instance Segmentation and Object Detection for Autonomous Driving | Dingfu Zhou, Jin Fang, Xibin Song, Liu Liu, Junbo Yin, Yuchao Dai, Hongdong Li, Ruigang Yang | Currently, in Autonomous Driving (AD), most of the 3D object detection frameworks (either anchor- or anchor-free-based) consider the detection as a Bounding Box (BBox) regression problem. However, this compact representation is not sufficient to explore all the information of the objects. To tackle this problem, we propose a simple but practical detection framework to jointly predict the 3D BBox and instance segmentation. For instance segmentation, we propose a Spatial Embeddings (SEs) strategy to assemble all foreground points into their corresponding object centers. Base on the SE results, the object proposals can be generated based on a simple clustering strategy. For each cluster, only one proposal is generated. Therefore, the Non-Maximum Suppression (NMS) process is no longer needed here. Finally, with our proposed instance-aware ROI pooling, the BBox is refined by a second-stage network. Experimental results on the public KITTI dataset show that the proposed SEs can significantly improve the instance segmentation results compared with other feature embedding-based method. Meanwhile, it also outperforms most of the 3D object detectors on the KITTI testing benchmark. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhou_Joint_3D_Instance_Segmentation_and_Object_Detection_for_Autonomous_Driving_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Joint_3D_Instance_Segmentation_and_Object_Detection_for_Autonomous_Driving_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Joint_3D_Instance_Segmentation_and_Object_Detection_for_Autonomous_Driving_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
DUNIT: Detection-Based Unsupervised Image-to-Image Translation | Deblina Bhattacharjee, Seungryong Kim, Guillaume Vizier, Mathieu Salzmann | Image-to-image translation has made great strides in recent years, with current techniques being able to handle unpaired training images and to account for the multi-modality of the translation problem. Despite this, most methods treat the image as a whole, which makes the results they produce for content-rich scenes less realistic. In this paper, we introduce a Detection-based Unsupervised Image-to-image Translation (DUNIT) approach that explicitly accounts for the object instances in the translation process. To this end, we extract separate representations for the global image and for the instances, which we then fuse into a common representation from which we generate the translated image. This allows us to preserve the detailed content of object instances, while still modeling the fact that we aim to produce an image of a single consistent scene. We introduce an instance consistency loss to maintain the coherence between the detections. Furthermore, by incorporating a detector into our architecture, we can still exploit object instances at test time. As evidenced by our experiments, this allows us to outperform the state-of-the-art unsupervised image-to-image translation methods. Furthermore, our approach can also be used as an unsupervised domain adaptation strategy for object detection, and it also achieves state-of-the-art performance on this task. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Bhattacharjee_DUNIT_Detection-Based_Unsupervised_Image-to-Image_Translation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Bhattacharjee_DUNIT_Detection-Based_Unsupervised_Image-to-Image_Translation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Bhattacharjee_DUNIT_Detection-Based_Unsupervised_Image-to-Image_Translation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Self-Supervised Human Depth Estimation From Monocular Videos | Feitong Tan, Hao Zhu, Zhaopeng Cui, Siyu Zhu, Marc Pollefeys, Ping Tan | Previous methods on estimating detailed human depth often require supervised training with 'ground truth' depth data. This paper presents a self-supervised method that can be trained on YouTube videos without known depth, which makes training data collection simple and improves the generalization of the learned network. The self-supervised learning is achieved by minimizing a photo-consistency loss, which is evaluated between a video frame and its neighboring frames warped according to the estimated depth and the 3D non-rigid motion of the human body. To solve this non-rigid motion, we first estimate a rough SMPL model at each video frame and compute the non-rigid body motion accordingly, which enables self-supervised learning on estimating the shape details. Experiments demonstrate that our method enjoys better generalization, and performs much better on data in the wild. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tan_Self-Supervised_Human_Depth_Estimation_From_Monocular_Videos_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.03358 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tan_Self-Supervised_Human_Depth_Estimation_From_Monocular_Videos_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tan_Self-Supervised_Human_Depth_Estimation_From_Monocular_Videos_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Tan_Self-Supervised_Human_Depth_CVPR_2020_supplemental.zip | null | null |
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid Decoder | Guanlin Li, Shuya Ding, Jun Luo, Chang Liu | Whereas adversarial training is employed as the main defence strategy against specific adversarial samples, it has limited generalization capability and incurs excessive time complexity. In this paper, we propose an attack-agnostic defence framework to enhance the intrinsic robustness of neural networks, without jeopardizing the ability of generalizing clean samples. Our Feature Pyramid Decoder (FPD) framework applies to all block-based convolutional neural networks (CNNs). It implants denoising and image restoration modules into a targeted CNN, and it also constraints the Lipschitz constant of the classification layer. Moreover, we propose a two-phase strategy to train the FPD-enhanced CNN, utilizing e-neighbourhood noisy images with multi-task and self-supervised learning. Evaluated against a variety of white-box and black-box attacks, we demonstrate that FPD-enhanced CNNs gain sufficient robustness against general adversarial samples on MNIST, SVHN and CALTECH. In addition, if we further conduct adversarial training, the FPD-enhanced CNNs perform better than their non-enhanced versions. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Enhancing_Intrinsic_Adversarial_Robustness_via_Feature_Pyramid_Decoder_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.02552 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Enhancing_Intrinsic_Adversarial_Robustness_via_Feature_Pyramid_Decoder_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Enhancing_Intrinsic_Adversarial_Robustness_via_Feature_Pyramid_Decoder_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Enhancing_Intrinsic_Adversarial_CVPR_2020_supplemental.pdf | null | null |
Two-Shot Spatially-Varying BRDF and Shape Estimation | Mark Boss, Varun Jampani, Kihwan Kim, Hendrik P.A. Lensch, Jan Kautz | Capturing the shape and spatially-varying appearance (SVBRDF) of an object from images is a challenging task that has applications in both computer vision and graphics. Traditional optimization-based approaches often need a large number of images taken from multiple views in a controlled environment. Newer deep learning-based approaches require only a few input images, but the reconstruction quality is not on par with optimization techniques. We propose a novel deep learning architecture with a stage-wise estimation of shape and SVBRDF. The previous predictions guide each estimation, and a joint refinement network later refines both SVBRDF and shape. We follow a practical mobile image capture setting and use unaligned two-shot flash and no-flash images as input. Both our two-shot image capture and network inference can run on mobile hardware. We also create a large-scale synthetic training dataset with domain-randomized geometry and realistic materials. Extensive experiments on both synthetic and real-world datasets show that our network trained on a synthetic dataset can generalize well to real-world images. Comparisons with recent approaches demonstrate the superior performance of the proposed approach. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Boss_Two-Shot_Spatially-Varying_BRDF_and_Shape_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00403 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Boss_Two-Shot_Spatially-Varying_BRDF_and_Shape_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Boss_Two-Shot_Spatially-Varying_BRDF_and_Shape_Estimation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Boss_Two-Shot_Spatially-Varying_BRDF_CVPR_2020_supplemental.pdf | null | null |
Polarized Non-Line-of-Sight Imaging | Kenichiro Tanaka, Yasuhiro Mukaigawa, Achuta Kadambi | This paper presents a method of passive non-line-of-sight (NLOS) imaging using polarization cues. A key observation is that the oblique light has a different polarimetric signal. It turns out this effect is due to the polarization axis rotation, a phenomena which can be used to better condition the light transport matrix for non-line-of-sight imaging. Our analysis and results show that the use of a polarization for NLOS is both a standalone technique, as well as an enhancement technique to boost the results of other forms of passive NLOS imaging. We make a surprising finding that, despite 50% light attenuation from polarization optics, the gains from polarized NLOS are overall superior to unpolarized NLOS. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tanaka_Polarized_Non-Line-of-Sight_Imaging_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tanaka_Polarized_Non-Line-of-Sight_Imaging_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tanaka_Polarized_Non-Line-of-Sight_Imaging_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Tanaka_Polarized_Non-Line-of-Sight_Imaging_CVPR_2020_supplemental.pdf | null | null |
End-to-End Optimization of Scene Layout | Andrew Luo, Zhoutong Zhang, Jiajun Wu, Joshua B. Tenenbaum | We propose an end-to-end variational generative model for scene layout synthesis conditioned on scene graphs. Unlike unconditional scene layout generation, we use scene graphs as an abstract but general representation to guide the synthesis of diverse scene layouts that satisfy relationships included in the scene graph. This gives rise to more flexible control over the synthesis process, allowing various forms of inputs such as scene layouts extracted from sentences or inferred from a single color image. Using our conditional layout synthesizer, we can generate various layouts that share the same structure of the input example. In addition to this conditional generation design, we also integrate a differentiable rendering module that enables layout refinement using only 2D projections of the scene. Given a depth and a semantics map, the differentiable rendering module enables optimizing over the synthesized layout to fit the given input in an analysis-by-synthesis fashion. Experiments suggest that our model achieves higher accuracy and diversity in conditional scene synthesis and allows exemplar-based scene generation from various input forms. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Luo_End-to-End_Optimization_of_Scene_Layout_CVPR_2020_paper.pdf | http://arxiv.org/abs/2007.11744 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Luo_End-to-End_Optimization_of_Scene_Layout_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Luo_End-to-End_Optimization_of_Scene_Layout_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection | Maosheng Ye, Shuangjie Xu, Tongyi Cao | We present Hybrid Voxel Network (HVNet), a novel one-stage unified network for point cloud based 3D object detection for autonomous driving. Recent studies show that 2D voxelization with per voxel PointNet style feature extractor leads to accurate and efficient detector for large 3D scenes. Since the size of the feature map determines the computation and memory cost, the size of the voxel becomes a parameter that is hard to balance. A smaller voxel size gives a better performance, especially for small objects, but a longer inference time. A larger voxel can cover the same area with a smaller feature map, but fails to capture intricate features and accurate location for smaller objects. We present a Hybrid Voxel network that solves this problem by fusing voxel feature encoder (VFE) of different scales at point-wise level and project into multiple pseudo-image feature maps. We further propose an attentive voxel feature encoding that outperforms plain VFE and a feature fusion pyramid network to aggregate multi-scale information at feature map level. Experiments on the KITTI benchmark show that a single HVNet achieves the best mAP among all existing methods with a real time inference speed of 31Hz. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ye_HVNet_Hybrid_Voxel_Network_for_LiDAR_Based_3D_Object_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.00186 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ye_HVNet_Hybrid_Voxel_Network_for_LiDAR_Based_3D_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ye_HVNet_Hybrid_Voxel_Network_for_LiDAR_Based_3D_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Action Modifiers: Learning From Adverbs in Instructional Videos | Hazel Doughty, Ivan Laptev, Walterio Mayol-Cuevas, Dima Damen | We present a method to learn a representation for adverbs from instructional videos using weak supervision from the accompanying narrations. Key to our method is the fact that the visual representation of the adverb is highly dependent on the action to which it applies, although the same adverb will modify multiple actions in a similar way. For instance, while 'spread quickly' and 'mix quickly' will look dissimilar, we can learn a common representation that allows us to recognize both, among other actions. We formulate this as an embedding problem, and use scaled dot product attention to learn from weakly-supervised video narrations. We jointly learn adverbs as invertible transformations which operate on the embedding space, so as to add or remove the effect of the adverb. As there is no prior work on weakly supervised learning from adverbs, we gather paired action-adverb annotations from a subset of the HowTo100M dataset, for 6 adverbs: quickly/slowly, finely/coarsely and partially/completely. Our method outperforms all baselines for video-to-adverb retrieval with a performance of 0.719 mAP. We also demonstrate our model's ability to attend to the relevant video parts in order to determine the adverb for a given action. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Doughty_Action_Modifiers_Learning_From_Adverbs_in_Instructional_Videos_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Doughty_Action_Modifiers_Learning_From_Adverbs_in_Instructional_Videos_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Doughty_Action_Modifiers_Learning_From_Adverbs_in_Instructional_Videos_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Doughty_Action_Modifiers_Learning_CVPR_2020_supplemental.zip | null | null |
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement | Chunle Guo, Chongyi Li, Jichang Guo, Chen Change Loy, Junhui Hou, Sam Kwong, Runmin Cong | The paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network. Our method trains a lightweight deep network, DCE-Net, to estimate pixel-wise and high-order curves for dynamic range adjustment of a given image. The curve estimation is specially designed, considering pixel value range, monotonicity, and differentiability. Zero-DCE is appealing in its relaxed assumption on reference images, i.e., it does not require any paired or unpaired data during training. This is achieved through a set of carefully formulated non-reference loss functions, which implicitly measure the enhancement quality and drive the learning of the network. Our method is efficient as image enhancement can be achieved by an intuitive and simple nonlinear curve mapping. Despite its simplicity, we show that it generalizes well to diverse lighting conditions. Extensive experiments on various benchmarks demonstrate the advantages of our method over state-of-the-art methods qualitatively and quantitatively. Furthermore, the potential benefits of our Zero-DCE to face detection in the dark are discussed. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Guo_Zero-Reference_Deep_Curve_Estimation_for_Low-Light_Image_Enhancement_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.06826 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Zero-Reference_Deep_Curve_Estimation_for_Low-Light_Image_Enhancement_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Zero-Reference_Deep_Curve_Estimation_for_Low-Light_Image_Enhancement_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Guo_Zero-Reference_Deep_Curve_CVPR_2020_supplemental.pdf | null | null |
FPConv: Learning Local Flattening for Point Convolution | Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han | We introduce FPConv, a novel surface-style convolution operator designed for 3D point cloud analysis. Unlike previous methods, FPConv doesn't require transforming to intermediate representation like 3D grid or graph and directly works on surface geometry of point cloud. To be more specific, for each point, FPConv performs a local flattening by automatically learning a weight map to softly project surrounding points onto a 2D grid. Regular 2D convolution can thus be applied for efficient feature learning. FPConv can be easily integrated into various network architectures for tasks like 3D object classification and 3D scene segmentation, and achieve comparable performance with existing volumetric-type convolutions. More importantly, our experiments also show that FPConv can be a complementary of volumetric convolutions and jointly training them can further boost overall performance into state-of-the-art results. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lin_FPConv_Learning_Local_Flattening_for_Point_Convolution_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.10701 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_FPConv_Learning_Local_Flattening_for_Point_Convolution_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_FPConv_Learning_Local_Flattening_for_Point_Convolution_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lin_FPConv_Learning_Local_CVPR_2020_supplemental.pdf | null | null |
View-GCN: View-Based Graph Convolutional Network for 3D Shape Analysis | Xin Wei, Ruixuan Yu, Jian Sun | View-based approach that recognizes 3D shape through its projected 2D images has achieved state-of-the-art results for 3D shape recognition. The major challenge for view-based approach is how to aggregate multi-view features to be a global shape descriptor. In this work, we propose a novel view-based Graph Convolutional Neural Network, dubbed as view-GCN, to recognize 3D shape based on graph representation of multiple views in flexible view configurations. We first construct view-graph with multiple views as graph nodes, then design a graph convolutional neural network over view-graph to hierarchically learn discriminative shape descriptor considering relations of multiple views. The view-GCN is a hierarchical network based on local and non-local graph convolution for feature transform, and selective view-sampling for graph coarsening. Extensive experiments on benchmark datasets show that view-GCN achieves state-of-the-art results for 3D shape classification and retrieval. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wei_View-GCN_View-Based_Graph_Convolutional_Network_for_3D_Shape_Analysis_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wei_View-GCN_View-Based_Graph_Convolutional_Network_for_3D_Shape_Analysis_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wei_View-GCN_View-Based_Graph_Convolutional_Network_for_3D_Shape_Analysis_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wei_View-GCN_View-Based_Graph_CVPR_2020_supplemental.pdf | null | null |
Footprints and Free Space From a Single Color Image | Jamie Watson, Michael Firman, Aron Monszpart, Gabriel J. Brostow | Understanding the shape of a scene from a single color image is a formidable computer vision task. However, most methods aim to predict the geometry of surfaces that are visible to the camera, which is of limited use when planning paths for robots or augmented reality agents. Such agents can only move when grounded on a traversable surface, which we define as the set of classes which humans can also walk over, such as grass, footpaths and pavement. Models which predict beyond the line of sight often parameterize the scene with voxels or meshes, which can be expensive to use in machine learning frameworks. We introduce a model to predict the geometry of both visible and occluded traversable surfaces, given a single RGB image as input. We learn from stereo video sequences, using camera poses, per-frame depth and semantic segmentation to form training data, which is used to supervise an image-to-image network. We train models from the KITTI driving dataset, the indoor Matterport dataset, and from our own casually captured stereo footage. We find that a surprisingly low bar for spatial coverage of training scenes is required. We validate our algorithm against a range of strong baselines, and include an assessment of our predictions for a path-planning task. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Watson_Footprints_and_Free_Space_From_a_Single_Color_Image_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.06376 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Watson_Footprints_and_Free_Space_From_a_Single_Color_Image_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Watson_Footprints_and_Free_Space_From_a_Single_Color_Image_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Towards Efficient Model Compression via Learned Global Ranking | Ting-Wu Chin, Ruizhou Ding, Cha Zhang, Diana Marculescu | Pruning convolutional filters has demonstrated its effectiveness in compressing ConvNets. Prior art in filter pruning requires users to specify a target model complexity (e.g., model size or FLOP count) for the resulting architecture. However, determining a target model complexity can be difficult for optimizing various embodied AI applications such as autonomous robots, drones, and user-facing applications. First, both the accuracy and the speed of ConvNets can affect the performance of the application. Second, the performance of the application can be hard to assess without evaluating ConvNets during inference. As a consequence, finding a sweet-spot between the accuracy and speed via filter pruning, which needs to be done in a trial-and-error fashion, can be time-consuming. This work takes a first step toward making this process more efficient by altering the goal of model compression to producing a set of ConvNets with various accuracy and latency trade-offs instead of producing one ConvNet targeting some pre-defined latency constraint. To this end, we propose to learn a global ranking of the filters across different layers of the ConvNet, which is used to obtain a set of ConvNet architectures that have different accuracy/latency trade-offs by pruning the bottom-ranked filters. Our proposed algorithm, LeGR, is shown to be 2x to 3x faster than prior work while having comparable or better performance when targeting seven pruned ResNet-56 with different accuracy/FLOPs profiles on the CIFAR-100 dataset. Additionally, we have evaluated LeGR on ImageNet and Bird-200 with ResNet-50 and Mo- bileNetV2 to demonstrate its effectiveness. Code available at https://github.com/cmu-enyac/LeGR. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chin_Towards_Efficient_Model_Compression_via_Learned_Global_Ranking_CVPR_2020_paper.pdf | http://arxiv.org/abs/1904.12368 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chin_Towards_Efficient_Model_Compression_via_Learned_Global_Ranking_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chin_Towards_Efficient_Model_Compression_via_Learned_Global_Ranking_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chin_Towards_Efficient_Model_CVPR_2020_supplemental.pdf | null | null |
Learning Multiview 3D Point Cloud Registration | Zan Gojcic, Caifa Zhou, Jan D. Wegner, Leonidas J. Guibas, Tolga Birdal | We present a novel, end-to-end learnable, multiview 3D point cloud registration algorithm. Registration of multiple scans typically follows a two-stage pipeline: the initial pairwise alignment and the globally consistent refinement. The former is often ambiguous due to the low overlap of neighboring point clouds, symmetries and repetitive scene parts. Therefore, the latter global refinement aims at establishing the cyclic consistency across multiple scans and helps in resolving the ambiguous cases. In this paper we propose, to the best of our knowledge, the first end-to-end algorithm for joint learning of both parts of this two-stage problem. Experimental evaluation on well accepted benchmark datasets shows that our approach outperforms the state-of-the-art by a significant margin, while being end-to-end trainable and computationally less costly. Moreover, we present detailed analysis and an ablation study that validate the novel components of our approach. The source code and pretrained models are publicly available under https://github.com/zgojcic/3D_multiview_reg. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gojcic_Learning_Multiview_3D_Point_Cloud_Registration_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.05119 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gojcic_Learning_Multiview_3D_Point_Cloud_Registration_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gojcic_Learning_Multiview_3D_Point_Cloud_Registration_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Gojcic_Learning_Multiview_3D_CVPR_2020_supplemental.pdf | null | null |
Transformation GAN for Unsupervised Image Synthesis and Representation Learning | Jiayu Wang, Wengang Zhou, Guo-Jun Qi, Zhongqian Fu, Qi Tian, Houqiang Li | Generative Adversarial Networks (GAN) have shown promising performance in image synthesis and unsupervised learning (USL). In most cases, however, the representations extracted from unsupervised GAN are usually unsatisfactory in other computer vision tasks. By using conditional GAN (CGAN), this problem could be solved to some extent, but the main drawback of such models is the necessity for labeled data. To improve both image synthesis quality and representation learning performance under the unsupervised setting, in this paper, we propose a simple yet effective Transformation Generative Adversarial Networks (TrGAN). In our approach, instead of capturing the joint distribution of image-label pairs p(x,y) as in conditional GAN, we try to estimate the joint distribution of transformed image t(x) and transformation t. Specifically, given a randomly sampled transformation t, we train the discriminator to give an estimate of input transformation, while following the adversarial training scheme of the original GAN. In addition, intermediate feature matching as well as feature-transform matching methods are introduced to strengthen the regularization on the generated features. To evaluate the quality of both generated samples and extracted representations, extensive experiments are conducted on four public datasets. The experimental results on the quality of both the synthesized images and the extracted representations demonstrate the effectiveness of our method. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Transformation_GAN_for_Unsupervised_Image_Synthesis_and_Representation_Learning_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=xsuX3BpZvlA | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Transformation_GAN_for_Unsupervised_Image_Synthesis_and_Representation_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Transformation_GAN_for_Unsupervised_Image_Synthesis_and_Representation_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Robustness Guarantees for Deep Neural Networks on Videos | Min Wu, Marta Kwiatkowska | The widespread adoption of deep learning models places demands on their robustness. In this paper, we consider the robustness of deep neural networks on videos, which comprise both the spatial features of individual frames extracted by a convolutional neural network and the temporal dynamics between adjacent frames captured by a recurrent neural network. To measure robustness, we study the maximum safe radius problem, which computes the minimum distance from the optical flow sequence obtained from a given input to that of an adversarial example in the neighbourhood of the input. We demonstrate that, under the assumption of Lipschitz continuity, the problem can be approximated using finite optimisation via discretising the optical flow space, and the approximation has provable guarantees. We then show that the finite optimisation problem can be solved by utilising a two-player turn-based game in a cooperative setting, where the first player selects the optical flows and the second player determines the dimensions to be manipulated in the chosen flow. We employ an anytime approach to solve the game, in the sense of approximating the value of the game by monotonically improving its upper and lower bounds. We exploit a gradient-based search algorithm to compute the upper bounds, and the admissible A* algorithm to update the lower bounds. Finally, we evaluate our framework on the UCF101 video dataset. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wu_Robustness_Guarantees_for_Deep_Neural_Networks_on_Videos_CVPR_2020_paper.pdf | http://arxiv.org/abs/1907.00098 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_Robustness_Guarantees_for_Deep_Neural_Networks_on_Videos_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_Robustness_Guarantees_for_Deep_Neural_Networks_on_Videos_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wu_Robustness_Guarantees_for_CVPR_2020_supplemental.pdf | null | null |
Neural Data Server: A Large-Scale Search Engine for Transfer Learning Data | Xi Yan, David Acuna, Sanja Fidler | Transfer learning has proven to be a successful technique to train deep learning models in the domains where little training data is available. The dominant approach is to pretrain a model on a large generic dataset such as ImageNet and finetune its weights on the target domain. However, in the new era of an ever-increasing number of massive datasets, selecting the relevant data for pretraining is a critical issue. We introduce Neural Data Server (NDS), a large-scale search engine for finding the most useful transfer learning data to the target domain. NDS consists of a dataserver which indexes several large popular image datasets, and aims to recommend data to a client, an end-user with a target application with its own small labeled dataset. The dataserver represents large datasets with a much more compact mixture-of-experts model, and employs it to perform data search in a series of dataserver-client transactions at a low computational cost. We show the effectiveness of NDS in various transfer learning scenarios, demonstrating state-of-the-art performance on several target datasets and tasks such as image classification, object detection and instance segmentation. Neural Data Server is available as a web-service at http://aidemo s.cs.toronto.edu/nds/. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yan_Neural_Data_Server_A_Large-Scale_Search_Engine_for_Transfer_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.02799 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yan_Neural_Data_Server_A_Large-Scale_Search_Engine_for_Transfer_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yan_Neural_Data_Server_A_Large-Scale_Search_Engine_for_Transfer_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yan_Neural_Data_Server_CVPR_2020_supplemental.pdf | null | null |
Instance Guided Proposal Network for Person Search | Wenkai Dong, Zhaoxiang Zhang, Chunfeng Song, Tieniu Tan | Person detection networks have been widely used in person search. These detectors discriminate persons from the background and generate proposals of all the persons from a gallery of scene images for each query. However, such a large number of proposals have a negative influence on the following identity matching process because many distractors are involved. In this paper, we propose a new detection network for person search, named Instance Guided Proposal Network (IGPN), which can learn the similarity between query persons and proposals. Thus, we can decrease proposals according to the similarity scores. To incorporate information of the query into the detection network, we introduce the Siamese region proposal network to Faster-RCNN and we propose improved cross-correlation layers to alleviate the imbalance of parameters distribution. Furthermore, we design a local relation block and a global relation branch to leverage the proposal-proposal relations and query-scene relations, respectively. Extensive experiments show that our method improves the person search performance through decreasing proposals and achieves competitive performance on two large person search benchmark datasets, CUHK-SYSU and PRW. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Dong_Instance_Guided_Proposal_Network_for_Person_Search_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Dong_Instance_Guided_Proposal_Network_for_Person_Search_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Dong_Instance_Guided_Proposal_Network_for_Person_Search_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Deep Representation Learning on Long-Tailed Data: A Learnable Embedding Augmentation Perspective | Jialun Liu, Yifan Sun, Chuchu Han, Zhaopeng Dou, Wenhui Li | This paper considers learning deep features from long-tailed data. We observe that in the deep feature space, the head classes and the tail classes present different distribution patterns. The head classes have a relatively large spatial span, while the tail classes have a significantly small spatial span, due to the lack of intra-class diversity. This uneven distribution between head and tail classes distorts the overall feature space, which compromises the discriminative ability of the learned features. In response, we seek to expand the distribution of the tail classes during training, so as to alleviate the distortion of the feature space. To this end, we propose to augment each instance of the tail classes with certain disturbances in the deep feature space. With the augmentation, a specified feature vector becomes a set of probable features scattered around itself, which is analogical to an atomic nucleus surrounded by the electron cloud. Intuitively, we name it as "feature cloud". The intra-class distribution of the feature cloud is learned from the head classes, and thus provides higher intra-class variation to the tail classes. Consequentially, it alleviates the distortion of the learned feature space, and improves deep representation learning on long tailed data. Extensive experimental evaluations on person re-identification and face recognition tasks confirm the effectiveness of our method. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Deep_Representation_Learning_on_Long-Tailed_Data_A_Learnable_Embedding_Augmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.10826 | https://www.youtube.com/watch?v=poaZO5N1J18 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Deep_Representation_Learning_on_Long-Tailed_Data_A_Learnable_Embedding_Augmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Deep_Representation_Learning_on_Long-Tailed_Data_A_Learnable_Embedding_Augmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Non-Line-of-Sight Surface Reconstruction Using the Directional Light-Cone Transform | Sean I. Young, David B. Lindell, Bernd Girod, David Taubman, Gordon Wetzstein | We propose a joint albedo-normal approach to non-line-of-sight (NLOS) surface reconstruction using the directional light-cone transform (D-LCT). While current NLOS imaging methods reconstruct either the albedo or surface normals of the hidden scene, the two quantities provide complementary information of the scene, so an efficient method to estimate both simultaneously is desirable. We formulate the recovery of the two quantities as a vector deconvolution problem, and solve it via Cholesky-Wiener decomposition. We demonstrate that surfaces fitted non-parametrically using our recovered normals are more accurate than those produced with NLOS surface reconstruction methods recently proposed, and are 1,000 times faster to compute than using inverse rendering. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Young_Non-Line-of-Sight_Surface_Reconstruction_Using_the_Directional_Light-Cone_Transform_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=K2H4DarHMv0 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Young_Non-Line-of-Sight_Surface_Reconstruction_Using_the_Directional_Light-Cone_Transform_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Young_Non-Line-of-Sight_Surface_Reconstruction_Using_the_Directional_Light-Cone_Transform_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Young_Non-Line-of-Sight_Surface_Reconstruction_CVPR_2020_supplemental.pdf | null | null |
IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning | Xi Yang, Ding Xia, Taichi Kin, Takeo Igarashi | Medicine is an important application area for deep learning models. Research in this field is a combination of medical expertise and data science knowledge. In this paper, instead of 2D medical images, we introduce an open-access 3D intracranial aneurysm dataset, IntrA, that makes the application of points-based and mesh-based classification and segmentation models available. Our dataset can be used to diagnose intracranial aneurysms and to extract the neck for a clipping operation in medicine and other areas of deep learning, such as normal estimation and surface reconstruction. We provide a large-scale benchmark of classification and part segmentation by testing state-of-the-art networks. We also discuss the performance of each method and demonstrate the challenges of our dataset. The published dataset can be accessed here: https://github.com/intra2d2019/IntrA. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_IntrA_3D_Intracranial_Aneurysm_Dataset_for_Deep_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.02920 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_IntrA_3D_Intracranial_Aneurysm_Dataset_for_Deep_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_IntrA_3D_Intracranial_Aneurysm_Dataset_for_Deep_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yang_IntrA_3D_Intracranial_CVPR_2020_supplemental.pdf | null | null |
3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset | Malte Pedersen, Joakim Bruslund Haurum, Stefan Hein Bengtson, Thomas B. Moeslund | In this work we present a novel publicly available stereo based 3D RGB dataset for multi-object zebrafish tracking, called 3D-ZeF. Zebrafish is an increasingly popular model organism used for studying neurological disorders, drug addiction, and more. Behavioral analysis is often a critical part of such research. However, visual similarity, occlusion, and erratic movement of the zebrafish makes robust 3D tracking a challenging and unsolved problem. The proposed dataset consists of eight sequences with a duration between 15-120 seconds and 1-10 free moving zebrafish. The videos have been annotated with a total of 86,400 points and bounding boxes. Furthermore, we present a complexity score and a novel open-source modular baseline system for 3D tracking of zebrafish. The performance of the system is measured with respect to two detectors: a naive approach and a Faster R-CNN based fish head detector. The system reaches a MOTA of up to 77.6%. Links to the code and dataset is available at the project page http://vap.aau.dk/3d-zef | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Pedersen_3D-ZeF_A_3D_Zebrafish_Tracking_Benchmark_Dataset_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Pedersen_3D-ZeF_A_3D_Zebrafish_Tracking_Benchmark_Dataset_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Pedersen_3D-ZeF_A_3D_Zebrafish_Tracking_Benchmark_Dataset_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Pedersen_3D-ZeF_A_3D_CVPR_2020_supplemental.pdf | https://cove.thecvf.com/datasets/362 | null |
X3D: Expanding Architectures for Efficient Video Recognition | Christoph Feichtenhofer | This paper presents X3D, a family of efficient video networks that progressively expand a tiny 2D image classification architecture along multiple network axes, in space, time, width and depth. Inspired by feature selection methods in machine learning, a simple stepwise network expansion approach is employed that expands a single axis in each step, such that good accuracy to complexity trade-off is achieved. To expand X3D to a specific target complexity, we perform progressive forward expansion followed by backward contraction. X3D achieves state-of-the-art performance while requiring 4.8x and 5.5x fewer multiply-adds and parameters for similar accuracy as previous work. Our most surprising finding is that networks with high spatiotemporal resolution can perform well, while being extremely light in terms of network width and parameters. We report competitive accuracy at unprecedented efficiency on video classification and detection benchmarks. Code is available at: https://github.com/facebookresearch/SlowFast. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Feichtenhofer_X3D_Expanding_Architectures_for_Efficient_Video_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.04730 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Feichtenhofer_X3D_Expanding_Architectures_for_Efficient_Video_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Feichtenhofer_X3D_Expanding_Architectures_for_Efficient_Video_Recognition_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Feichtenhofer_X3D_Expanding_Architectures_CVPR_2020_supplemental.pdf | null | null |
Unsupervised Intra-Domain Adaptation for Semantic Segmentation Through Self-Supervision | Fei Pan, Inkyu Shin, Francois Rameau, Seokju Lee, In So Kweon | Convolutional neural network-based approaches have achieved remarkable progress in semantic segmentation. However, these approaches heavily rely on annotated data which are labor intensive. To cope with this limitation, automatically annotated data generated from graphic engines are used to train segmentation models. However, the models trained from synthetic data are difficult to transfer to real images. To tackle this issue, previous works have considered directly adapting models from the source data to the unlabeled target data (to reduce the inter-domain gap). Nonetheless, these techniques do not consider the large distribution gap among the target data itself (intra-domain gap). In this work, we propose a two-step self-supervised domain adaptation approach to minimize the inter-domain and intra-domain gap together. First, we conduct the inter-domain adaptation of the model, from this adaptation, we separate target domain into an easy and hard split using an entropy-based ranking function. Finally, to decrease the intra-domain gap, we propose to employ a self-supervised adaptation technique from the easy to the hard subdomain. Experimental results on numerous benchmark datasets highlight the effectiveness of our method against existing state-of-the-art approaches. The source code is available at https://github.com/feipan664/IntraDA.git. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Pan_Unsupervised_Intra-Domain_Adaptation_for_Semantic_Segmentation_Through_Self-Supervision_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.07703 | https://www.youtube.com/watch?v=4vPr8RTAauI | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Pan_Unsupervised_Intra-Domain_Adaptation_for_Semantic_Segmentation_Through_Self-Supervision_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Pan_Unsupervised_Intra-Domain_Adaptation_for_Semantic_Segmentation_Through_Self-Supervision_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Something-Else: Compositional Action Recognition With Spatial-Temporal Interaction Networks | Joanna Materzynska, Tete Xiao, Roei Herzig, Huijuan Xu, Xiaolong Wang, Trevor Darrell | Human action is naturally compositional: humans can easily recognize and perform actions with objects that are different from those used in training demonstrations. In this paper, we study the compositionality of action by looking into the dynamics of subject-object interactions. We propose a novel model which can explicitly reason about the geometric relations between constituent objects and an agent performing an action. To train our model, we collect dense object box annotations on the Something-Something dataset. We propose a novel compositional action recognition task where the training combinations of verbs and nouns do not overlap with the test set. The novel aspects of our model are applicable to activities with prominent object interaction dynamics and to objects which can be tracked using state-of-the-art approaches; for activities without clearly defined spatial object-agent interactions, we rely on baseline scene-level spatio-temporal representations. We show the effectiveness of our approach not only on the proposed compositional action recognition task but also in a few-shot compositional setting which requires the model to generalize across both object appearance and action category. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Materzynska_Something-Else_Compositional_Action_Recognition_With_Spatial-Temporal_Interaction_Networks_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=5fph1WkOfjU | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Materzynska_Something-Else_Compositional_Action_Recognition_With_Spatial-Temporal_Interaction_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Materzynska_Something-Else_Compositional_Action_Recognition_With_Spatial-Temporal_Interaction_Networks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Materzynska_Something-Else_Compositional_Action_CVPR_2020_supplemental.pdf | https://cove.thecvf.com/datasets/352 | null |
Exploring Spatial-Temporal Multi-Frequency Analysis for High-Fidelity and Temporal-Consistency Video Prediction | Beibei Jin, Yu Hu, Qiankun Tang, Jingyu Niu, Zhiping Shi, Yinhe Han, Xiaowei Li | Video prediction is a pixel-wise dense prediction task to infer future frames based on past frames. Missing appearance details and motion blur are still two major problems for current models, leading to image distortion and temporal inconsistency. We point out the necessity of exploring multi-frequency analysis to deal with the two problems. Inspired by the frequency band decomposition characteristic of Human Vision System (HVS), we propose a video prediction network based on multi-level wavelet analysis to uniformly deal with spatial and temporal information. Specifically, multi-level spatial discrete wavelet transform decomposes each video frame into anisotropic sub-bands with multiple frequencies, helping to enrich structural information and reserve fine details. On the other hand, multilevel temporal discrete wavelet transform which operates on time axis decomposes the frame sequence into sub-band groups of different frequencies to accurately capture multifrequency motions under a fixed frame rate. Extensive experiments on diverse datasets demonstrate that our model shows significant improvements on fidelity and temporal consistency over the state-of-the-art works. Source code and videos are available at https://github.com/Bei-Jin/STMFANet. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jin_Exploring_Spatial-Temporal_Multi-Frequency_Analysis_for_High-Fidelity_and_Temporal-Consistency_Video_Prediction_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.09905 | https://www.youtube.com/watch?v=cemetXMOInU | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jin_Exploring_Spatial-Temporal_Multi-Frequency_Analysis_for_High-Fidelity_and_Temporal-Consistency_Video_Prediction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jin_Exploring_Spatial-Temporal_Multi-Frequency_Analysis_for_High-Fidelity_and_Temporal-Consistency_Video_Prediction_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud | Weijing Shi, Raj Rajkumar | In this paper, we propose a graph neural network to detect objects from a LiDAR point cloud. Towards this end, we encode the point cloud efficiently in a fixed radius near-neighbors graph. We design a graph neural network, named Point-GNN, to predict the category and shape of the object that each vertex in the graph belongs to. In Point-GNN, we propose an auto-registration mechanism to reduce translation variance, and also design a box merging and scoring operation to combine detections from multiple vertices accurately. Our experiments on the KITTI benchmark show the proposed approach achieves leading accuracy using the point cloud alone and can even surpass fusion-based algorithms. Our results demonstrate the potential of using the graph neural network as a new approach for 3D object detection. The code is available at https://github.com/WeijingShi/Point-GNN. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shi_Point-GNN_Graph_Neural_Network_for_3D_Object_Detection_in_a_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Shi_Point-GNN_Graph_Neural_Network_for_3D_Object_Detection_in_a_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Shi_Point-GNN_Graph_Neural_Network_for_3D_Object_Detection_in_a_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Shi_Point-GNN_Graph_Neural_CVPR_2020_supplemental.pdf | null | null |
How Much Time Do You Have? Modeling Multi-Duration Saliency | Camilo Fosco, Anelise Newman, Pat Sukhum, Yun Bin Zhang, Nanxuan Zhao, Aude Oliva, Zoya Bylinskii | What jumps out in a single glance of an image is different than what you might notice after closer inspection. Yet conventional models of visual saliency produce predictions at an arbitrary, fixed viewing duration, offering a limited view of the rich interactions between image content and gaze location. In this paper we propose to capture gaze as a series of snapshots, by generating population-level saliency heatmaps for multiple viewing durations. We collect the CodeCharts1K dataset, which contains multiple distinct heatmaps per image corresponding to 0.5, 3, and 5 seconds of free-viewing. We develop an LSTM-based model of saliency that simultaneously trains on data from multiple viewing durations. Our Multi-Duration Saliency Excited Model (MD-SEM) achieves competitive performance on the LSUN 2017 Challenge with 57% fewer parameters than comparable architectures. It is the first model that produces heatmaps at multiple viewing durations, enabling applications where multi-duration saliency can be used to prioritize visual content to keep, transmit, and render. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Fosco_How_Much_Time_Do_You_Have_Modeling_Multi-Duration_Saliency_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Fosco_How_Much_Time_Do_You_Have_Modeling_Multi-Duration_Saliency_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Fosco_How_Much_Time_Do_You_Have_Modeling_Multi-Duration_Saliency_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Fosco_How_Much_Time_CVPR_2020_supplemental.pdf | null | null |
Learning Fused Pixel and Feature-Based View Reconstructions for Light Fields | Jinglei Shi, Xiaoran Jiang, Christine Guillemot | In this paper, we present a learning-based framework for light field view synthesis from a subset of input views. Building upon a light-weight optical flow estimation network to obtain depth maps, our method employs two reconstruction modules in pixel and feature domains respectively. For the pixel-wise reconstruction, occlusions are explicitly handled by a disparity-dependent interpolation filter, whereas inpainting on disoccluded areas is learned by convolutional layers. Due to disparity inconsistencies, the pixel-based reconstruction may lead to blurriness in highly textured areas as well as on object contours. On the contrary, the feature-based reconstruction well performs on high frequencies, making the reconstruction in the two domains complementary. End-to-end learning is finally performed including a fusion module merging pixel and feature-based reconstructions. Experimental results show that our method achieves state-of-the-art performance on both synthetic and real-world datasets, moreover, it is even able to extend light fields' baseline by extrapolating high quality views without additional training. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shi_Learning_Fused_Pixel_and_Feature-Based_View_Reconstructions_for_Light_Fields_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=JFLDYnPRl04 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Shi_Learning_Fused_Pixel_and_Feature-Based_View_Reconstructions_for_Light_Fields_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Shi_Learning_Fused_Pixel_and_Feature-Based_View_Reconstructions_for_Light_Fields_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Shi_Learning_Fused_Pixel_CVPR_2020_supplemental.pdf | null | null |
Google Landmarks Dataset v2 - A Large-Scale Benchmark for Instance-Level Recognition and Retrieval | Tobias Weyand, Andre Araujo, Bingyi Cao, Jack Sim | While image retrieval and instance recognition techniques are progressing rapidly, there is a need for challenging datasets to accurately measure their performance -- while posing novel challenges that are relevant for practical applications. We introduce the Google Landmarks Dataset v2 (GLDv2), a new benchmark for large-scale, fine-grained instance recognition and image retrieval in the domain of human-made and natural landmarks. GLDv2 is the largest such dataset to date by a large margin, including over 5M images and 200k distinct instance labels. Its test set consists of 118k images with ground truth annotations for both the retrieval and recognition tasks. The ground truth construction involved over 800 hours of human annotator work. Our new dataset has several challenging properties inspired by real-world applications that previous datasets did not consider: An extremely long-tailed class distribution, a large fraction of out-of-domain test photos and large intra-class variability. The dataset is sourced from Wikimedia Commons, the world's largest crowdsourced collection of landmark photos. We provide baseline results for both recognition and retrieval tasks based on state-of-the-art methods as well as competitive results from a public challenge. We further demonstrate the suitability of the dataset for transfer learning by showing that image embeddings trained on it achieve competitive retrieval performance on independent datasets. The dataset images, ground-truth and metric scoring code are available at https://github.com/cvdfoundation/google-landmark | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Weyand_Google_Landmarks_Dataset_v2_-_A_Large-Scale_Benchmark_for_Instance-Level_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=lINRczsp8i0 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Weyand_Google_Landmarks_Dataset_v2_-_A_Large-Scale_Benchmark_for_Instance-Level_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Weyand_Google_Landmarks_Dataset_v2_-_A_Large-Scale_Benchmark_for_Instance-Level_CVPR_2020_paper.html | CVPR 2020 | null | https://cove.thecvf.com/datasets/350 | null |
Lightweight Photometric Stereo for Facial Details Recovery | Xueying Wang, Yudong Guo, Bailin Deng, Juyong Zhang | Recently, 3D face reconstruction from a single image has achieved great success with the help of deep learning and shape prior knowledge, but they often fail to produce accurate geometry details. On the other hand, photometric stereo methods can recover reliable geometry details, but require dense inputs and need to solve a complex optimization problem. In this paper, we present a lightweight strategy that only requires sparse inputs or even a single image to recover high-fidelity face shapes with images captured under near-field lights. To this end, we construct a dataset containing 84 different subjects with 29 expressions under 3 different lights. Data augmentation is applied to enrich the data in terms of diversity in identity, lighting, expression, etc. With this constructed dataset, we propose a novel neural network specially designed for photometric stereo based 3D face reconstruction. Extensive experiments and comparisons demonstrate that our method can generate high-quality reconstruction results with one to three facial images captured under near-field lights. Our full framework is available at https://github.com/Juyong/FacePSNet. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Lightweight_Photometric_Stereo_for_Facial_Details_Recovery_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12307 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Lightweight_Photometric_Stereo_for_Facial_Details_Recovery_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Lightweight_Photometric_Stereo_for_Facial_Details_Recovery_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_Lightweight_Photometric_Stereo_CVPR_2020_supplemental.pdf | null | null |
SAL: Sign Agnostic Learning of Shapes From Raw Data | Matan Atzmon, Yaron Lipman | Recently, neural networks have been used as implicit representations for surface reconstruction, modelling, learning, and generation. So far, training neural networks to be implicit representations of surfaces required training data sampled from a ground-truth signed implicit functions such as signed distance or occupancy functions, which are notoriously hard to compute. In this paper we introduce Sign Agnostic Learning (SAL), a deep learning approach for learning implicit shape representations directly from raw, unsigned geometric data, such as point clouds and triangle soups. We have tested SAL on the challenging problem of surface reconstruction from an un-oriented point cloud, as well as end-to-end human shape space learning directly from raw scans dataset, and achieved state of the art reconstructions compared to current approaches. We believe SAL opens the door to many geometric deep learning applications with real-world data, alleviating the usual painstaking, often manual pre-process. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Atzmon_SAL_Sign_Agnostic_Learning_of_Shapes_From_Raw_Data_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.10414 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Atzmon_SAL_Sign_Agnostic_Learning_of_Shapes_From_Raw_Data_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Atzmon_SAL_Sign_Agnostic_Learning_of_Shapes_From_Raw_Data_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Atzmon_SAL_Sign_Agnostic_CVPR_2020_supplemental.pdf | null | null |
Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration | Yang He, Yuhang Ding, Ping Liu, Linchao Zhu, Hanwang Zhang, Yi Yang | Filter pruning has been widely applied to neural network compression and acceleration. Existing methods usually utilize pre-defined pruning criteria, such as Lp-norm, to prune unimportant filters. There are two major limitations to these methods. First, existing methods fail to consider the variety of filter distribution across layers. To extract features of the coarse level to the fine level, the filters of different layers have various distributions. Therefore, it is not suitable to utilize the same pruning criteria to different functional layers. Second, prevailing layer-by-layer pruning methods process each layer independently and sequentially, failing to consider that all the layers in the network collaboratively make the final prediction. In this paper, we propose Learning Filter Pruning Criteria (LFPC) to solve the above problems. Specifically, we develop a differentiable pruning criteria sampler. This sampler is learnable and optimized by the validation loss of the pruned network obtained from the sampled criteria. In this way, we could adaptively select the appropriate pruning criteria for different functional layers. Besides, when evaluating the sampled criteria, LFPC comprehensively consider the contribution of all the layers at the same time. Experiments validate our approach on three image classification benchmarks. Notably, on ILSVRC-2012, our LFPC reduces more than 60% FLOPs on ResNet-50 with only 0.83% top-5 accuracy loss. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/He_Learning_Filter_Pruning_Criteria_for_Deep_Convolutional_Neural_Networks_Acceleration_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=HGB3QS1Xli8 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/He_Learning_Filter_Pruning_Criteria_for_Deep_Convolutional_Neural_Networks_Acceleration_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/He_Learning_Filter_Pruning_Criteria_for_Deep_Convolutional_Neural_Networks_Acceleration_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/He_Learning_Filter_Pruning_CVPR_2020_supplemental.pdf | null | null |
WCP: Worst-Case Perturbations for Semi-Supervised Deep Learning | Liheng Zhang, Guo-Jun Qi | In this paper, we present a novel regularization mechanism for training deep networks by minimizing the Worse-Case Perturbation (WCP). It is based on the idea that a robust model is least likely to be affected by small perturbations, such that its output decisions should be as stable as possible on both labeled and unlabeled examples. We will consider two forms of WCP regularizations -- additive and DropConnect perturbations, which impose additive noises on network weights, and make structural changes by dropping the network connections, respectively. We will show that the worse cases of both perturbations can be derived by solving respective optimization problems with spectral methods. The WCP can be minimized on both labeled and unlabeled data so that networks can be trained in a semi-supervised fashion. This leads to a novel paradigm of semi-supervised classifiers by stabilizing the predicted outputs in presence of the worse-case perturbations imposed on the network weights and structures. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_WCP_Worst-Case_Perturbations_for_Semi-Supervised_Deep_Learning_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_WCP_Worst-Case_Perturbations_for_Semi-Supervised_Deep_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_WCP_Worst-Case_Perturbations_for_Semi-Supervised_Deep_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Separating Particulate Matter From a Single Microscopic Image | Tushar Sandhan, Jin Young Choi | Particulate matter (PM) is the blend of various solid and liquid particles suspended in atmosphere. These submicron particles are imperceptible for usual hand-held camera photography, but become a great obstacle in microscopic imaging. PM removal from a single microscopic image is a highly ill-posed and one of the challenging image denoising problems. In this work, we thoroughly analyze the physical properties of PM, microscope and their inevitable interaction; and propose an optimization scheme, which removes the PM from a high-resolution microscopic image within a few seconds. Experiments on real world microscopic images show that the proposed method significantly outperforms other competitive image denoising methods. It preserves the comprehensive microscopic foreground details while clearly separating the PM from a single monochromatic or color image. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Sandhan_Separating_Particulate_Matter_From_a_Single_Microscopic_Image_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Sandhan_Separating_Particulate_Matter_From_a_Single_Microscopic_Image_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Sandhan_Separating_Particulate_Matter_From_a_Single_Microscopic_Image_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Event Probability Mask (EPM) and Event Denoising Convolutional Neural Network (EDnCNN) for Neuromorphic Cameras | R. Wes Baldwin, Mohammed Almatrafi, Vijayan Asari, Keigo Hirakawa | This paper presents a novel method for labeling real-world neuromorphic camera sensor data by calculating the likelihood of generating an event at each pixel within a short time window, which we refer to as "event probability mask" or EPM. Its applications include (i) objective benchmarking of event denoising performance, (ii) training convolutional neural networks for noise removal called "event denoising convolutional neural network" (EDnCNN), and (iii) estimating internal neuromorphic camera parameters. We provide the first dataset (DVSNOISE20) of real-world labeled neuromorphic camera events for noise removal. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Baldwin_Event_Probability_Mask_EPM_and_Event_Denoising_Convolutional_Neural_Network_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.08282 | https://www.youtube.com/watch?v=HR6P7PxgGQY | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Baldwin_Event_Probability_Mask_EPM_and_Event_Denoising_Convolutional_Neural_Network_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Baldwin_Event_Probability_Mask_EPM_and_Event_Denoising_Convolutional_Neural_Network_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Baldwin_Event_Probability_Mask_CVPR_2020_supplemental.zip | https://cove.thecvf.com/datasets/318 | null |
Peek-a-Boo: Occlusion Reasoning in Indoor Scenes With Plane Representations | Ziyu Jiang, Buyu Liu, Samuel Schulter, Zhangyang Wang, Manmohan Chandraker | We address the challenging task of occlusion-aware indoor 3D scene understanding. We represent scenes by a set of planes, where each one is defined by its normal, offset and two masks outlining (i) the extent of the visible part and (ii) the full region that consists of both visible and occluded parts of the plane. We infer these planes from a single input image with a novel neural network architecture. It consists of a two-branch category-specific module that aims to predict layout and objects of the scene separately so that different types of planes can be handled better. We also introduce a novel loss function based on plane warping that can leverage multiple views at training time for improved occlusion-aware reasoning. In order to train and evaluate our occlusion-reasoning model, we use the ScanNet dataset and propose (i) a strategy to automatically extract ground truth for both visible and hidden regions and (ii) a new evaluation metric that specifically focuses on the prediction in hidden regions. We empirically demonstrate that our proposed approach can achieve higher accuracy for occlusion reasoning compared to competitive baselines on the ScanNet dataset, e.g. 42.65% relative improvement on hidden regions. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jiang_Peek-a-Boo_Occlusion_Reasoning_in_Indoor_Scenes_With_Plane_Representations_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=-lFsnm0zvP8 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_Peek-a-Boo_Occlusion_Reasoning_in_Indoor_Scenes_With_Plane_Representations_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_Peek-a-Boo_Occlusion_Reasoning_in_Indoor_Scenes_With_Plane_Representations_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Jiang_Peek-a-Boo_Occlusion_Reasoning_CVPR_2020_supplemental.pdf | null | null |
Unsupervised Learning of Probably Symmetric Deformable 3D Objects From Images in the Wild | Shangzhe Wu, Christian Rupprecht, Andrea Vedaldi | We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision. The method is based on an autoencoder that factors each input image into depth, albedo, viewpoint and illumination. In order to disentangle these components without supervision, we use the fact that many object categories have, at least in principle, a symmetric structure. We show that reasoning about illumination allows us to exploit the underlying object symmetry even if the appearance is not symmetric due to shading. Furthermore, we model objects that are probably, but not certainly, symmetric by predicting a symmetry probability map, learned end-to-end with the other components of the model. Our experiments show that this method can recover very accurately the 3D shape of human faces, cat faces and cars from single-view images, without any supervision or a prior shape model. On benchmarks, we demonstrate superior accuracy compared to another method that uses supervision at the level of 2D image correspondences. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wu_Unsupervised_Learning_of_Probably_Symmetric_Deformable_3D_Objects_From_Images_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.11130 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_Unsupervised_Learning_of_Probably_Symmetric_Deformable_3D_Objects_From_Images_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_Unsupervised_Learning_of_Probably_Symmetric_Deformable_3D_Objects_From_Images_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wu_Unsupervised_Learning_of_CVPR_2020_supplemental.pdf | null | null |
Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Images | Noriaki Hashimoto, Daisuke Fukushima, Ryoichi Koga, Yusuke Takagi, Kaho Ko, Kei Kohno, Masato Nakaguro, Shigeo Nakamura, Hidekata Hontani, Ichiro Takeuchi | We propose a new method for cancer subtype classification from histopathological images, which can automatically detect tumor-specific features in a given whole slide image (WSI). The cancer subtype should be classified by referring to a WSI, i.e., a large-sized image (typically 40,000x40,000 pixels) of an entire pathological tissue slide, which consists of cancer and non-cancer portions. One difficulty arises from the high cost associated with annotating tumor regions in WSIs. Furthermore, both global and local image features must be extracted from the WSI by changing the magnifications of the image. In addition, the image features should be stably detected against the differences of staining conditions among the hospitals/specimens. In this paper, we develop a new CNN-based cancer subtype classification method by effectively combining multiple-instance, domain adversarial, and multi-scale learning frameworks in order to overcome these practical difficulties. When the proposed method was applied to malignant lymphoma subtype classifications of 196 cases collected from multiple hospitals, the classification performance was significantly better than the standard CNN or other conventional methods, and the accuracy compared favorably with that of standard pathologists. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hashimoto_Multi-scale_Domain-adversarial_Multiple-instance_CNN_for_Cancer_Subtype_Classification_with_Unannotated_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.01599 | https://www.youtube.com/watch?v=O6bWMIQXcfM | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hashimoto_Multi-scale_Domain-adversarial_Multiple-instance_CNN_for_Cancer_Subtype_Classification_with_Unannotated_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hashimoto_Multi-scale_Domain-adversarial_Multiple-instance_CNN_for_Cancer_Subtype_Classification_with_Unannotated_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
STAViS: Spatio-Temporal AudioVisual Saliency Network | Antigoni Tsiami, Petros Koutras, Petros Maragos | We introduce STAViS, a spatio-temporal audiovisual saliency network that combines spatio-temporal visual and auditory information in order to efficiently address the problem of saliency estimation in videos. Our approach employs a single network that combines visual saliency and auditory features and learns to appropriately localize sound sources and to fuse the two saliencies in order to obtain a final saliency map. The network has been designed, trained end-to-end, and evaluated on six different databases that contain audiovisual eye-tracking data of a large variety of videos. We compare our method against 8 different state-of-the-art visual saliency models. Evaluation results across databases indicate that our STAViS model outperforms our visual only variant as well as the other state-of-the-art models in the majority of cases. Also, the consistently good performance it achieves for all databases indicates that it is appropriate for estimating saliency "in-the-wild". The code is available at https://github.com/atsiami/STAViS. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tsiami_STAViS_Spatio-Temporal_AudioVisual_Saliency_Network_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.03063 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tsiami_STAViS_Spatio-Temporal_AudioVisual_Saliency_Network_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tsiami_STAViS_Spatio-Temporal_AudioVisual_Saliency_Network_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Tsiami_STAViS_Spatio-Temporal_AudioVisual_CVPR_2020_supplemental.zip | null | null |
OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression | Lila Huang, Shenlong Wang, Kelvin Wong, Jerry Liu, Raquel Urtasun | We present a novel deep compression algorithm to reduce the memory footprint of LiDAR point clouds. Our method exploits the sparsity and structural redundancy between points to reduce the bitrate. Towards this goal, we first encode the point cloud into an octree, a data-efficient structure suitable for sparse point clouds. We then design a tree-structured conditional entropy model that can be directly applied to octree structures to predict the probability of a symbol's occurrence. We validate the effectiveness of our method over two large-scale datasets. The results demonstrate that our approach reduces the bitrate by 10- 20% at the same reconstruction quality, compared to the previous state-of-the-art. Importantly, we also show that for the same bitrate, our approach outperforms other compression algorithms when performing downstream 3D segmentation and detection tasks using compressed representations. This helps advance the feasibility of using point cloud compression to reduce the onboard and offboard storage for safety-critical applications such as self-driving cars, where a single vehicle captures 84 billion points per day. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Huang_OctSqueeze_Octree-Structured_Entropy_Model_for_LiDAR_Compression_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.07178 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_OctSqueeze_Octree-Structured_Entropy_Model_for_LiDAR_Compression_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_OctSqueeze_Octree-Structured_Entropy_Model_for_LiDAR_Compression_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Huang_OctSqueeze_Octree-Structured_Entropy_CVPR_2020_supplemental.zip | null | null |
Block-Wisely Supervised Neural Architecture Search With Knowledge Distillation | Changlin Li, Jiefeng Peng, Liuchun Yuan, Guangrun Wang, Xiaodan Liang, Liang Lin, Xiaojun Chang | Neural Architecture Search (NAS), aiming at automatically designing network architectures by machines, is expected to bring about a new revolution in machine learning. Despite these high expectation, the effectiveness and efficiency of existing NAS solutions are unclear, with some recent works going so far as to suggest that many existing NAS solutions are no better than random architecture selection. The ineffectiveness of NAS solutions may be attributed to inaccurate architecture evaluation. Specifically, to speed up NAS, recent works have proposed under-training different candidate architectures in a large search space concurrently by using shared network parameters; however, this has resulted in incorrect architecture ratings and furthered the ineffectiveness of NAS. In this work, we propose to modularize the large search space of NAS into blocks to ensure that the potential candidate architectures are fully trained; this reduces the representation shift caused by the shared parameters and leads to the correct rating of the candidates. Thanks to the block-wise search, we can also evaluate all of the candidate architectures within each block. Moreover, we find that the knowledge of a network model lies not only in the network parameters but also in the network architecture. Therefore, we propose to distill the neural architecture (DNA) knowledge from a teacher model to supervise our block-wise architecture search, which significantly improves the effectiveness of NAS. Remarkably, the performance of our searched architectures has exceeded the teacher model, demonstrating the practicability of our method. Finally, our method achieves a state-of-the-art 78.4% top-1 accuracy on ImageNet in a mobile setting. All of our searched models along with the evaluation code are available at https://github.com/changlin31/DNA. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Block-Wisely_Supervised_Neural_Architecture_Search_With_Knowledge_Distillation_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=DCBYhdCPpoA | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Block-Wisely_Supervised_Neural_Architecture_Search_With_Knowledge_Distillation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Block-Wisely_Supervised_Neural_Architecture_Search_With_Knowledge_Distillation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Block-Wisely_Supervised_Neural_CVPR_2020_supplemental.pdf | null | null |
PANDA: A Gigapixel-Level Human-Centric Video Dataset | Xueyang Wang, Xiya Zhang, Yinheng Zhu, Yuchen Guo, Xiaoyun Yuan, Liuyu Xiang, Zerun Wang, Guiguang Ding, David Brady, Qionghai Dai, Lu Fang | We present PANDA, the first gigaPixel-level humAN-centric viDeo dAtaset, for large-scale, long-term, and multi-object visual analysis. The videos in PANDA were captured by a gigapixel camera and cover real-world scenes with both wide field-of-view ( 1 square kilometer area) and high-resolution details ( gigapixel-level/frame). The scenes may contain 4k head counts with over 100x scale variation. PANDA provides enriched and hierarchical ground-truth annotations, including 15,974.6k bounding boxes, 111.8k fine-grained attribute labels, 12.7k trajectories, 2.2k groups and 2.9k interactions. We benchmark the human detection and tracking tasks. Due to the vast variance of pedestrian pose, scale, occlusion and trajectory, existing approaches are challenged by both accuracy and efficiency. Given the uniqueness of PANDA with both wide FoV and high resolution, a new task of interaction-aware group detection is introduced. We design a 'global-to-local zoom-in' framework, where global trajectories and local interactions are simultaneously encoded, yielding promising results. We believe PANDA will contribute to the community of artificial intelligence and praxeology by understanding human behaviors and interactions in large-scale real-world scenes. PANDA Website: http://www.panda-dataset.com. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_PANDA_A_Gigapixel-Level_Human-Centric_Video_Dataset_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.04852 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_PANDA_A_Gigapixel-Level_Human-Centric_Video_Dataset_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_PANDA_A_Gigapixel-Level_Human-Centric_Video_Dataset_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_PANDA_A_Gigapixel-Level_CVPR_2020_supplemental.pdf | https://cove.thecvf.com/datasets/313 | null |
Few-Shot Learning of Part-Specific Probability Space for 3D Shape Segmentation | Lingjing Wang, Xiang Li, Yi Fang | Recently, deep neural networks are introduced as supervised discriminative models for the learning of 3D point cloud segmentation. Most previous supervised methods require a large number of training data with human annotation part labels to guide the training process to ensure the model's generalization abilities on test data. In comparison, we propose a novel 3D shape segmentation method that requires few labeled data for training. Given an input 3D shape, the training of our model starts with identifying a similar 3D shape with part annotations from a mini-pool of shape templates (e.g. 10 shapes). With the selected template shape, a novel Coherent Point Transformer is proposed to fully leverage the power of a deep neural network to smoothly morph the template shape towards the input shape. Then, based on the transformed template shapes with part labels, a newly proposed Part-specific Density Estimator is developed to learn a continuous part-specific probability distribution function on the entire 3D space with a batch consistency regularization term. With the learned part-specific probability distribution, our model is able to predict the part labels of a new input 3D shape in an end-to-end manner. We demonstrate that our proposed method can achieve remarkable segmentation results on the ShapeNet dataset with few shots, compared to previous supervised learning approaches. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Few-Shot_Learning_of_Part-Specific_Probability_Space_for_3D_Shape_Segmentation_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=oWbHimeNtQA | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Few-Shot_Learning_of_Part-Specific_Probability_Space_for_3D_Shape_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Few-Shot_Learning_of_Part-Specific_Probability_Space_for_3D_Shape_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Actor-Transformers for Group Activity Recognition | Kirill Gavrilyuk, Ryan Sanford, Mehrsan Javan, Cees G. M. Snoek | This paper strives to recognize individual actions and group activities from videos. While existing solutions for this challenging problem explicitly model spatial and temporal relationships based on location of individual actors, we propose an actor-transformer model able to learn and selectively extract information relevant for group activity recognition. We feed the transformer with rich actor-specific static and dynamic representations expressed by features from a 2D pose network and 3D CNN, respectively. We empirically study different ways to combine these representations and show their complementary benefits. Experiments show what is important to transform and how it should be transformed. What is more, actor-transformers achieve state-of-the-art results on two publicly available benchmarks for group activity recognition, outperforming the previous best published results by a considerable margin | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gavrilyuk_Actor-Transformers_for_Group_Activity_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12737 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gavrilyuk_Actor-Transformers_for_Group_Activity_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gavrilyuk_Actor-Transformers_for_Group_Activity_Recognition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Domain Adaptation for Image Dehazing | Yuanjie Shao, Lerenhan Li, Wenqi Ren, Changxin Gao, Nong Sang | Image dehazing using learning-based methods has achieved state-of-the-art performance in recent years. However, most existing methods train a dehazing model on synthetic hazy images, which are less able to generalize well to real hazy images due to domain shift. To address this issue, we propose a domain adaptation paradigm, which consists of an image translation module and two image dehazing modules. Specifically, we first apply a bidirectional translation network to bridge the gap between the synthetic and real domains by translating images from one domain to another. And then, we use images before and after translation to train the proposed two image dehazing networks with a consistency constraint. In this phase, we incorporate the real hazy image into the dehazing training via exploiting the properties of the clear image (e.g., dark channel prior and image gradient smoothing) to further improve the domain adaptivity. By training image translation and dehazing network in an end-to-end manner, we can obtain better effects of both image translation and dehazing. Experimental results on both synthetic and real-world images demonstrate that our model performs favorably against the state-of-the-art dehazing algorithms. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shao_Domain_Adaptation_for_Image_Dehazing_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.04668 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Shao_Domain_Adaptation_for_Image_Dehazing_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Shao_Domain_Adaptation_for_Image_Dehazing_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Shao_Domain_Adaptation_for_CVPR_2020_supplemental.pdf | null | null |
Exploiting Joint Robustness to Adversarial Perturbations | Ali Dabouei, Sobhan Soleymani, Fariborz Taherkhani, Jeremy Dawson, Nasser M. Nasrabadi | Recently, ensemble models have demonstrated empirical capabilities to alleviate the adversarial vulnerability. In this paper, we exploit first-order interactions within ensembles to formalize a reliable and practical defense. We introduce a scenario of interactions that certifiably improves the robustness according to the size of the ensemble, the diversity of the gradient directions, and the balance of the member's contribution to the robustness. We present a joint gradient phase and magnitude regularization (GPMR) as a vigorous approach to impose the desired scenario of interactions among members of the ensemble. Through extensive experiments, including gradient-based and gradient-free evaluations on several datasets and network architectures, we validate the practical effectiveness of the proposed approach compared to the previous methods. Furthermore, we demonstrate that GPMR is orthogonal to other defense strategies developed for single classifiers and their combination can further improve the robustness of ensembles. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Dabouei_Exploiting_Joint_Robustness_to_Adversarial_Perturbations_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Dabouei_Exploiting_Joint_Robustness_to_Adversarial_Perturbations_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Dabouei_Exploiting_Joint_Robustness_to_Adversarial_Perturbations_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Advisable Learning for Self-Driving Vehicles by Internalizing Observation-to-Action Rules | Jinkyu Kim, Suhong Moon, Anna Rohrbach, Trevor Darrell, John Canny | Humans learn to drive through both practice and theory, e.g. by studying the rules, while most self-driving systems are limited to the former. Being able to incorporate human knowledge of typical causal driving behaviour should benefit autonomous systems. We propose a new approach that learns vehicle control with the help of human advice. Specifically, our system learns to summarize its visual observations in natural language, predict an appropriate action response (e.g. "I see a pedestrian crossing, so I stop"), and predict the controls, accordingly. Moreover, to enhance interpretability of our system, we introduce a fine-grained attention mechanism which relies on semantic segmentation and object-centric RoI pooling. We show that our approach of training the autonomous system with human advice, grounded in a rich semantic representation, matches or outperforms prior work in terms of control prediction and explanation generation. Our approach also results in more interpretable visual explanations by visualizing object-centric attention maps. Code is available at https://github.com/JinkyuKimUCB/advisable-driving. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kim_Advisable_Learning_for_Self-Driving_Vehicles_by_Internalizing_Observation-to-Action_Rules_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=hugV0OBxrP0 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Advisable_Learning_for_Self-Driving_Vehicles_by_Internalizing_Observation-to-Action_Rules_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Advisable_Learning_for_Self-Driving_Vehicles_by_Internalizing_Observation-to-Action_Rules_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kim_Advisable_Learning_for_CVPR_2020_supplemental.pdf | null | null |
Lightweight Multi-View 3D Pose Estimation Through Camera-Disentangled Representation | Edoardo Remelli, Shangchen Han, Sina Honari, Pascal Fua, Robert Wang | We present a lightweight solution to recover 3D pose from multi-view images captured with spatially calibrated cameras. Building upon recent advances in interpretable representation learning, we exploit 3D geometry to fuse input images into a unified latent representation of pose, which is disentangled from camera view-points. This allows us to reason effectively about 3D pose across different views without using compute-intensive volumetric grids. Our architecture then conditions the learned representation on camera projection operators to produce accurate per-view 2d detections, that can be simply lifted to 3D via a differentiable Direct Linear Transform (DLT) layer. In order to do it efficiently, we propose a novel implementation of DLT that is orders of magnitude faster on GPU architectures than standard SVD-based triangulation methods. We evaluate our approach on two large-scale human pose datasets (H36M and Total Capture): our method outperforms or performs comparably to the state-of-the-art volumetric methods, while, unlike them, yielding real-time performance. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Remelli_Lightweight_Multi-View_3D_Pose_Estimation_Through_Camera-Disentangled_Representation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.02186 | https://www.youtube.com/watch?v=KCxX72izBdA | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Remelli_Lightweight_Multi-View_3D_Pose_Estimation_Through_Camera-Disentangled_Representation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Remelli_Lightweight_Multi-View_3D_Pose_Estimation_Through_Camera-Disentangled_Representation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Remelli_Lightweight_Multi-View_3D_CVPR_2020_supplemental.zip | null | null |
Robust Design of Deep Neural Networks Against Adversarial Attacks Based on Lyapunov Theory | Arash Rahnama, Andre T. Nguyen, Edward Raff | Deep neural networks (DNNs) are vulnerable to subtle adversarial perturbations applied to the input. These adversarial perturbations, though imperceptible, can easily mislead the DNN. In this work, we take a control theoretic approach to the problem of robustness in DNNs. We treat each individual layer of the DNN as a nonlinear system and use Lyapunov theory to prove stability and robustness locally. We then proceed to prove stability and robustness globally for the entire DNN. We develop empirically tight bounds on the response of the output layer, or any hidden layer, to adversarial perturbations added to the input, or the input of hidden layers. Recent works have proposed spectral norm regularization as a solution for improving robustness against l2 adversarial attacks. Our results give new insights into how spectral norm regularization can mitigate the adversarial effects. Finally, we evaluate the power of our approach on a variety of data sets and network architectures and against some of the well-known adversarial attacks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Rahnama_Robust_Design_of_Deep_Neural_Networks_Against_Adversarial_Attacks_Based_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.04636 | https://www.youtube.com/watch?v=AJClIX11UYc | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Rahnama_Robust_Design_of_Deep_Neural_Networks_Against_Adversarial_Attacks_Based_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Rahnama_Robust_Design_of_Deep_Neural_Networks_Against_Adversarial_Attacks_Based_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Rahnama_Robust_Design_of_CVPR_2020_supplemental.zip | null | null |
Cross-Modal Deep Face Normals With Deactivable Skip Connections | Victoria Fernandez Abrevaya, Adnane Boukhayma, Philip H.S. Torr, Edmond Boyer | We present an approach for estimating surface normals from in-the-wild color images of faces. While data-driven strategies have been proposed for single face images, limited available ground truth data makes this problem difficult. To alleviate this issue, we propose a method that can leverage all available image and normal data, whether paired or not, thanks to a novel cross-modal learning architecture. In particular, we enable additional training with single modality data, either color or normal, by using two encoder-decoder networks with a shared latent space. The proposed architecture also enables face details to be transferred between the image and normal domains, given paired data, through skip connections between the image encoder and normal decoder. Core to our approach is a novel module that we call deactivable skip connections, which allows integrating both the auto-encoded and image-to-normal branches within the same architecture that can be trained end-to-end. This allows learning of a rich latent space that can accurately capture the normal information. We compare against state-of-the-art methods and show that our approach can achieve significant improvements, both quantitative and qualitative, with natural face images. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Abrevaya_Cross-Modal_Deep_Face_Normals_With_Deactivable_Skip_Connections_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.09691 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Abrevaya_Cross-Modal_Deep_Face_Normals_With_Deactivable_Skip_Connections_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Abrevaya_Cross-Modal_Deep_Face_Normals_With_Deactivable_Skip_Connections_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Abrevaya_Cross-Modal_Deep_Face_CVPR_2020_supplemental.pdf | null | null |
Progressive Adversarial Networks for Fine-Grained Domain Adaptation | Sinan Wang, Xinyang Chen, Yunbo Wang, Mingsheng Long, Jianmin Wang | Fine-grained visual categorization has long been considered as an important problem, however, its real application is still restricted, since precisely annotating a large fine-grained image dataset is a laborious task and requires expert-level human knowledge. A solution to this problem is applying domain adaptation approaches to fine-grained scenarios, where the key idea is to discover the commonality between existing fine-grained image datasets and massive unlabeled data in the wild. The main technical bottleneck lies in that the large inter-domain variation will deteriorate the subtle boundaries of small inter-class variation during domain alignment. This paper presents the Progressive Adversarial Networks (PAN) to align fine-grained categories across domains with a curriculum-based adversarial learning framework. In particular, throughout the learning process, domain adaptation is carried out through all multi-grained features, progressively exploiting the label hierarchy from coarse to fine. The progressive learning is applied upon both category classification and domain alignment, boosting both the discriminability and the transferability of the fine-grained features. Our method is evaluated on three benchmarks, two of which are proposed by us, and it outperforms the state-of-the-art domain adaptation methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Progressive_Adversarial_Networks_for_Fine-Grained_Domain_Adaptation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Progressive_Adversarial_Networks_for_Fine-Grained_Domain_Adaptation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Progressive_Adversarial_Networks_for_Fine-Grained_Domain_Adaptation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
ActBERT: Learning Global-Local Video-Text Representations | Linchao Zhu, Yi Yang | In this paper, we introduce ActBERT for self-supervised learning of joint video-text representations from unlabeled data. First, we leverage global action information to catalyze the mutual interactions between linguistic texts and local regional objects. It uncovers global and local visual clues from paired video sequences and text descriptions for detailed visual and text relation modeling. Second, we introduce an ENtangled Transformer block (ENT) to encode three sources of information, i.e., global actions, local regional objects, and linguistic descriptions. Global-local correspondences are discovered via judicious clues extraction from contextual information. It enforces the joint videotext representation to be aware of fine-grained objects as well as global human intention. We validate the generalization capability of ActBERT on downstream video-and language tasks, i.e., text-video clip retrieval, video captioning, video question answering, action segmentation, and action step localization. ActBERT significantly outperform the state-of-the-arts, demonstrating its superiority in video-text representation learning. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhu_ActBERT_Learning_Global-Local_Video-Text_Representations_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_ActBERT_Learning_Global-Local_Video-Text_Representations_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_ActBERT_Learning_Global-Local_Video-Text_Representations_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Towards Visually Explaining Variational Autoencoders | Wenqian Liu, Runze Li, Meng Zheng, Srikrishna Karanam, Ziyan Wu, Bir Bhanu, Richard J. Radke, Octavia Camps | Recent advances in Convolutional Neural Network (CNN) model interpretability have led to impressive progress in visualizing and understanding model predictions. In particular, gradient-based visual attention methods have driven much recent effort in using visual attention maps as a means for visual explanations. A key problem, however, is these methods are designed for classification and categorization tasks, and their extension to explaining generative models, e.g., variational autoencoders (VAE) is not trivial. In this work, we take a step towards bridging this crucial gap, proposing the first technique to visually explain VAEs by means of gradient-based attention. We present methods to generate visual attention from the learned latent space, and also demonstrate such attention explanations serve more than just explaining VAE predictions. We show how these attention maps can be used to localize anomalies in images, demonstrating state-of-the-art performance on the MVTec-AD dataset. We also show how they can be infused into model training, helping bootstrap the VAE into learning improved latent space disentanglement, demonstrated on the Dsprites dataset. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Towards_Visually_Explaining_Variational_Autoencoders_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.07389 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Towards_Visually_Explaining_Variational_Autoencoders_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Towards_Visually_Explaining_Variational_Autoencoders_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Liu_Towards_Visually_Explaining_CVPR_2020_supplemental.pdf | null | null |
CenterMask: Single Shot Instance Segmentation With Point Representation | Yuqing Wang, Zhaoliang Xu, Hao Shen, Baoshan Cheng, Lirong Yang | In this paper, we propose a single-shot instance segmentation method, which is simple, fast and accurate. There are two main challenges for one-stage instance segmentation: object instances differentiation and pixel-wise feature alignment. Accordingly, we decompose the instance segmentation into two parallel subtasks: Local Shape prediction that separates instances even in overlapping conditions, and Global Saliency generation that segments the whole image in a pixel-to-pixel manner. The outputs of the two branches are assembled to form the final instance masks. To realize that, the local shape information is adopted from the representation of object center points. Totally trained from scratch and without any bells and whistles, the proposed CenterMask achieves 34.5 mask AP with a speed of 12.3 fps, using a single-model with single-scale training/testing on the challenging COCO dataset. The accuracy is higher than all other one-stage instance segmentation methods except the 5 times slower TensorMask, which shows the effectiveness of CenterMask. Besides, our method can be easily embedded to other one-stage object detectors such as FCOS and performs well, showing the generation of CenterMask. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_CenterMask_Single_Shot_Instance_Segmentation_With_Point_Representation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.04446 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_CenterMask_Single_Shot_Instance_Segmentation_With_Point_Representation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_CenterMask_Single_Shot_Instance_Segmentation_With_Point_Representation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
BidNet: Binocular Image Dehazing Without Explicit Disparity Estimation | Yanwei Pang, Jing Nie, Jin Xie, Jungong Han, Xuelong Li | Heavy haze results in severe image degradation and thus hampers the performance of visual perception, object detection, etc. On the assumption that dehazed binocular images are superior to the hazy ones for stereo vision tasks such as 3D object detection and according to the fact that image haze is a function of depth, this paper proposes a Binocular image dehazing Network (BidNet) aiming at dehazing both the left and right images of binocular images within the deep learning framework. Existing binocular dehazing methods rely on simultaneously dehazing and estimating disparity, whereas BidNet does not need to explicitly perform time-consuming and well-known challenging disparity estimation. Note that a small error in disparity gives rise to a large variation in depth and in estimation of haze-free image. The relationship and correlation between binocular images are explored and encoded by the proposed Stereo Transformation Module (STM). Jointly dehazing binocular image pairs is mutually beneficial, which is better than only dehazing left images. We extend the Foggy Cityscapes dataset to a Stereo Foggy Cityscapes dataset with binocular foggy image pairs. Experimental results demonstrate that BidNet significantly outperforms state-of-the-art dehazing methods in both subjective and objective assessments. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Pang_BidNet_Binocular_Image_Dehazing_Without_Explicit_Disparity_Estimation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Pang_BidNet_Binocular_Image_Dehazing_Without_Explicit_Disparity_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Pang_BidNet_Binocular_Image_Dehazing_Without_Explicit_Disparity_Estimation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Unsupervised Learning From Video With Deep Neural Embeddings | Chengxu Zhuang, Tianwei She, Alex Andonian, Max Sobol Mark, Daniel Yamins | Because of the rich dynamical structure of videos andtheir ubiquity in everyday life, it is a natural idea that video data could serve as a powerful unsupervised learning signal for visual representations. However, instantiating this idea, especially at large scale, has remained a significant artificial intelligence challenge. Here we present the Video Instance Embedding (VIE) framework, which trains deep nonlinear embeddings on video sequence inputs. By learning embedding dimensions that identify and group similar videos together, while pushing inherently different videos apart in the embedding space, VIE captures the strong statistical structure inherent in videos, without the need for external annotation labels. We find that, when trained on a large-scale video dataset, VIE yields powerful representations both for action recognition and single-frame object categorization, showing substantially improving on the state of the art wherever direct comparisons are possible. We show that a two-pathway model with both static and dynamic processingpathways is optimal, provide analyses indicating how the model works, and perform ablation studies showing the importance of key architecture and loss function choices. Our results suggest that deep neural embeddings are a promising approach to unsupervised video learning fora wide variety of task domains. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhuang_Unsupervised_Learning_From_Video_With_Deep_Neural_Embeddings_CVPR_2020_paper.pdf | http://arxiv.org/abs/1905.11954 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhuang_Unsupervised_Learning_From_Video_With_Deep_Neural_Embeddings_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhuang_Unsupervised_Learning_From_Video_With_Deep_Neural_Embeddings_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhuang_Unsupervised_Learning_From_CVPR_2020_supplemental.pdf | null | null |
One-Shot Domain Adaptation for Face Generation | Chao Yang, Ser-Nam Lim | In this paper, we propose a framework capable of generating face images that fall into the same distribution as that of a given one-shot example. We leverage a pre-trained StyleGAN model that already learned the generic face distribution. Given the one-shot target, we develop an iterative optimization scheme that rapidly adapts the weights of the model to shift the output's high-level distribution to the target's. To generate images of the same distribution, we introduce a style-mixing technique that transfers the low-level statistics from the target to faces randomly generated with the model. With that, we are able to generate an unlimited number of faces that inherit from the distribution of both generic human faces and the one-shot example. The newly generated faces can serve as augmented training data for other downstream tasks. Such setting is appealing as it requires labeling very few, or even one example, in the target domain, which is often the case of real-world face manipulations that result from a variety of unknown and unique distributions, each with extremely low prevalence. We show the effectiveness of our one-shot approach for detecting face manipulations and compare it with other few-shot domain adaptation methods qualitatively and quantitatively. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_One-Shot_Domain_Adaptation_for_Face_Generation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12869 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_One-Shot_Domain_Adaptation_for_Face_Generation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_One-Shot_Domain_Adaptation_for_Face_Generation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
A Unified Optimization Framework for Low-Rank Inducing Penalties | Marcus Valtonen Ornhag, Carl Olsson | In this paper we study the convex envelopes of a new class of functions. Using this approach, we are able to unify two important classes of regularizers from unbiased non-convex formulations and weighted nuclear norm penalties. This opens up for possibilities of combining the best of both worlds, and to leverage each methods contribution to cases where simply enforcing one of the regularizers are insufficient. We show that the proposed regularizers can be incorporated in standard splitting schemes such as Alternating Direction Methods of Multipliers (ADMM), and other sub-gradient methods. This can be implemented efficiently since the the proximal operator can be computed fast. Furthermore, we show on real non-rigid structure from motion datasets, the issues that arise from using weighted nuclear norm penalties, and how this can be remedied using our proposed prior-free method. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ornhag_A_Unified_Optimization_Framework_for_Low-Rank_Inducing_Penalties_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.08415 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ornhag_A_Unified_Optimization_Framework_for_Low-Rank_Inducing_Penalties_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ornhag_A_Unified_Optimization_Framework_for_Low-Rank_Inducing_Penalties_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Ornhag_A_Unified_Optimization_CVPR_2020_supplemental.pdf | null | null |
Cost Volume Pyramid Based Depth Inference for Multi-View Stereo | Jiayu Yang, Wei Mao, Jose M. Alvarez, Miaomiao Liu | We propose a cost volume-based neural network for depth inference from multi-view images. We demonstrate that building a cost volume pyramid in a coarse-to-fine manner instead of constructing a cost volume at a fixed resolution leads to a compact, lightweight network and allows us inferring high resolution depth maps to achieve better reconstruction results. To this end, we first build a cost volume based on uniform sampling of fronto-parallel planes across the entire depth range at the coarsest resolution of an image. Then, given current depth estimate, we construct new cost volumes iteratively on the pixelwise depth residual to perform depth map refinement. While sharing similar insight with Point-MVSNet as predicting and refining depth iteratively, we show that working on cost volume pyramid can lead to a more compact, yet efficient network structure compared with the Point-MVSNet on 3D points. We further provide detailed analyses of the relation between (residual) depth sampling and image resolution, which serves as a principle for building compact cost volume pyramid. Experimental results on benchmark datasets show that our model can perform 6x faster and has similar performance as state-of-the-art methods. Code is available at https://github.com/JiayuYANG/CVP-MVSNet | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_Cost_Volume_Pyramid_Based_Depth_Inference_for_Multi-View_Stereo_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.08329 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Cost_Volume_Pyramid_Based_Depth_Inference_for_Multi-View_Stereo_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Cost_Volume_Pyramid_Based_Depth_Inference_for_Multi-View_Stereo_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yang_Cost_Volume_Pyramid_CVPR_2020_supplemental.pdf | null | null |
Learning for Video Compression With Hierarchical Quality and Recurrent Enhancement | Ren Yang, Fabian Mentzer, Luc Van Gool, Radu Timofte | In this paper, we propose a Hierarchical Learned Video Compression (HLVC) method with three hierarchical quality layers and a recurrent enhancement network. The frames in the first layer are compressed by an image compression method with the highest quality. Using these frames as references, we propose the Bi-Directional Deep Compression (BDDC) network to compress the second layer with relatively high quality. Then, the third layer frames are compressed with the lowest quality, by the proposed Single Motion Deep Compression (SMDC) network, which adopts a single motion map to estimate the motions of multiple frames, thus saving bits for motion information. In our deep decoder, we develop the Weighted Recurrent Quality Enhancement (WRQE) network, which takes both compressed frames and the bit stream as inputs. In the recurrent cell of WRQE, the memory and update signal are weighted by quality features to reasonably leverage multi-frame information for enhancement. In our HLVC approach, the hierarchical quality benefits the coding efficiency, since the high quality information facilitates the compression and enhancement of low quality frames at encoder and decoder sides, respectively. Finally, the experiments validate that our HLVC approach advances the state-of-the-art of deep video compression methods, and outperforms the "Low-Delay P (LDP) very fast" mode of x265 in terms of both PSNR and MS-SSIM. The project page is at https://github.com/RenYang-home/HLVC. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_Learning_for_Video_Compression_With_Hierarchical_Quality_and_Recurrent_Enhancement_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.01966 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Learning_for_Video_Compression_With_Hierarchical_Quality_and_Recurrent_Enhancement_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Learning_for_Video_Compression_With_Hierarchical_Quality_and_Recurrent_Enhancement_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yang_Learning_for_Video_CVPR_2020_supplemental.pdf | null | null |
Sub-Frame Appearance and 6D Pose Estimation of Fast Moving Objects | Denys Rozumnyi, Jan Kotera, Filip Sroubek, Jiri Matas | We propose a novel method that tracks fast moving objects, mainly non-uniform spherical, in full 6 degrees of freedom, estimating simultaneously their 3D motion trajectory, 3D pose and object appearance changes with a time step that is a fraction of the video frame exposure time. The sub-frame object localization and appearance estimation allows realistic temporal super-resolution and precise shape estimation. The method, called TbD-3D (Tracking by Deblatting in 3D) relies on a novel reconstruction algorithm which solves a piece-wise deblurring and matting problem. The 3D rotation is estimated by minimizing the reprojection error. As a second contribution, we present a new challenging dataset with fast moving objects that change their appearance and distance to the camera. High-speed camera recordings with zero lag between frame exposures were used to generate videos with different frame rates annotated with ground-truth trajectory and pose. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Rozumnyi_Sub-Frame_Appearance_and_6D_Pose_Estimation_of_Fast_Moving_Objects_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.10927 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Rozumnyi_Sub-Frame_Appearance_and_6D_Pose_Estimation_of_Fast_Moving_Objects_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Rozumnyi_Sub-Frame_Appearance_and_6D_Pose_Estimation_of_Fast_Moving_Objects_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Rozumnyi_Sub-Frame_Appearance_and_CVPR_2020_supplemental.zip | null | null |
TetraTSDF: 3D Human Reconstruction From a Single Image With a Tetrahedral Outer Shell | Hayato Onizuka, Zehra Hayirci, Diego Thomas, Akihiro Sugimoto, Hideaki Uchiyama, Rin-ichiro Taniguchi | Recovering the 3D shape of a person from its 2D appearance is ill-posed due to ambiguities. Nevertheless, with the help of convolutional neural networks (CNN) and prior knowledge on the 3D human body, it is possible to overcome such ambiguities to recover detailed 3D shapes of human bodies from single images. Current solutions, however, fail to reconstruct all the details of a person wearing loose clothes. This is because of either (a) huge memory requirement that cannot be maintained even on modern GPUs or (b) the compact 3D representation that cannot encode all the details. In this paper, we propose the tetrahedral outer shell volumetric truncated signed distance function (TetraTSDF) model for the human body, and its corresponding part connection network (PCN) for 3D human body shape regression. Our proposed model is compact, dense, accurate, and yet well suited for CNN-based regression task. Our proposed PCN allows us to learn the distribution of the TSDF in the tetrahedral volume from a single image in an end-to-end manner. Results show that our proposed method allows to reconstruct detailed shapes of humans wearing loose clothes from single RGB images. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Onizuka_TetraTSDF_3D_Human_Reconstruction_From_a_Single_Image_With_a_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.10534 | https://www.youtube.com/watch?v=sSqBsLAZS0U | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Onizuka_TetraTSDF_3D_Human_Reconstruction_From_a_Single_Image_With_a_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Onizuka_TetraTSDF_3D_Human_Reconstruction_From_a_Single_Image_With_a_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo Matching | Pengpeng Liu, Irwin King, Michael R. Lyu, Jia Xu | In this paper, we propose a unified method to jointly learn optical flow and stereo matching. Our first intuition is stereo matching can be modeled as a special case of optical flow, and we can leverage 3D geometry behind stereoscopic videos to guide the learning of these two forms of correspondences. We then enroll this knowledge into the state-of-the-art self-supervised learning framework, and train one single network to estimate both flow and stereo. Second, we unveil the bottlenecks in prior self-supervised learning approaches, and propose to create a new set of challenging proxy tasks to boost performance. These two insights yield a single model that achieves the highest accuracy among all existing unsupervised flow and stereo methods on KITTI 2012 and 2015 benchmarks. More remarkably, our self-supervised method even outperforms several state-of-the-art fully supervised methods, including PWC-Net and FlowNet2 on KITTI 2012. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Flow2Stereo_Effective_Self-Supervised_Learning_of_Optical_Flow_and_Stereo_Matching_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.02138 | https://www.youtube.com/watch?v=7O7qEJKhrws | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Flow2Stereo_Effective_Self-Supervised_Learning_of_Optical_Flow_and_Stereo_Matching_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Flow2Stereo_Effective_Self-Supervised_Learning_of_Optical_Flow_and_Stereo_Matching_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
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