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Single Image Optical Flow Estimation With an Event Camera | Liyuan Pan, Miaomiao Liu, Richard Hartley | Event cameras are bio-inspired sensors that asynchronously report intensity changes in microsecond resolution. DAVIS can capture high dynamics of a scene and simultaneously output high temporal resolution events and low frame-rate intensity images. In this paper, we propose a single image (potentially blurred) and events based optical flow estimation approach. First, we demonstrate how events can be used to improve flow estimates. To this end, we encode the relation between flow and events effectively by presenting an event-based photometric consistency formulation. Then, we consider the special case of image blur caused by high dynamics in the visual environments and show that including the blur formation in our model further constrains flow estimation. This is in sharp contrast to existing works that ignore the blurred images while our formulation can naturally handle either blurred or sharp images to achieve accurate flow estimation. Finally, we reduce flow estimation, as well as image deblurring, to an alternative optimization problem of an objective function using the primal-dual algorithm. Experimental results on both synthetic and real data (with blurred and non-blurred images) show the superiority of our model in comparison to state-of-the-art approaches. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Pan_Single_Image_Optical_Flow_Estimation_With_an_Event_Camera_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00347 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Pan_Single_Image_Optical_Flow_Estimation_With_an_Event_Camera_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Pan_Single_Image_Optical_Flow_Estimation_With_an_Event_Camera_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Fine-Grained Generalized Zero-Shot Learning via Dense Attribute-Based Attention | Dat Huynh, Ehsan Elhamifar | We address the problem of fine-grained generalized zero-shot recognition of visually similar classes without training images for some classes. We propose a dense attribute-based attention mechanism that for each attribute focuses on the most relevant image regions, obtaining attribute-based features. Instead of aligning a global feature vector of an image with its associated class semantic vector, we propose an attribute embedding technique that aligns each attribute-based feature with its attribute semantic vector. Hence, we compute a vector of attribute scores, for the presence of each attribute in an image, whose similarity with the true class semantic vector is maximized. Moreover, we adjust each attribute score using an attention mechanism over attributes to better capture the discriminative power of different attributes. To tackle the challenge of bias towards seen classes during testing, we propose a new self-calibration loss that adjusts the probability of unseen classes to account for the training bias. We conduct experiments on three popular datasets of CUB, SUN and AWA2 as well as the large-scale DeepFashion dataset, showing that our model significantly improves the state of the art. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Huynh_Fine-Grained_Generalized_Zero-Shot_Learning_via_Dense_Attribute-Based_Attention_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=Lq03ZdB6DXE | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Huynh_Fine-Grained_Generalized_Zero-Shot_Learning_via_Dense_Attribute-Based_Attention_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Huynh_Fine-Grained_Generalized_Zero-Shot_Learning_via_Dense_Attribute-Based_Attention_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Huynh_Fine-Grained_Generalized_Zero-Shot_CVPR_2020_supplemental.pdf | null | null |
Solving Jigsaw Puzzles With Eroded Boundaries | Dov Bridger, Dov Danon, Ayellet Tal | Jigsaw puzzle solving is an intriguing problem which has been explored in computer vision for decades. This paper focuses on a specific variant of the problem--solving puzzles with eroded boundaries. Such erosion makes the problem extremely difficult, since most existing solvers utilize solely the information at the boundaries. Nevertheless, this variant is important since erosion and missing data often occur at the boundaries. The key idea of our proposed approach is to inpaint the eroded boundaries between puzzle pieces and later leverage the quality of the inpainted area to classify a pair of pieces as "neighbors or not". An interesting feature of our architecture is that the same GAN discriminator is used for both inpainting and classification; training of the second task is simply a continuation of the training of the first, beginning from the point it left off. We show that our approach outperforms other SOTA methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Bridger_Solving_Jigsaw_Puzzles_With_Eroded_Boundaries_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.00755 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Bridger_Solving_Jigsaw_Puzzles_With_Eroded_Boundaries_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Bridger_Solving_Jigsaw_Puzzles_With_Eroded_Boundaries_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning Rank-1 Diffractive Optics for Single-Shot High Dynamic Range Imaging | Qilin Sun, Ethan Tseng, Qiang Fu, Wolfgang Heidrich, Felix Heide | High-dynamic range (HDR) imaging is an essential imaging modality for a wide range of applications in uncontrolled environments, including autonomous driving, robotics, and mobile phone cameras. However, existing HDR techniques in commodity devices struggle with dynamic scenes due to multi-shot acquisition and post-processing time, e.g. mobile phone burst photography, making such approaches unsuitable for real-time applications. In this work, we propose a method for snapshot HDR imaging by learning an optical HDR encoding in a single image which maps saturated highlights into neighboring unsaturated areas using a diffractive optical element (DOE). We propose a novel rank-1 parameterization of the proposed DOE which avoids vast trainable parameters and keeps high frequencies' encoding compared with conventional end-to-end design methods. We further propose a reconstruction network tailored to this rank-1 parametrization for recovery of clipped information from the encoded measurements. The proposed end-to-end framework is validated through simulation and real-world experiments and improves the PSNR by more than 7 dB over state-of-the-art end-to-end designs. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Sun_Learning_Rank-1_Diffractive_Optics_for_Single-Shot_High_Dynamic_Range_Imaging_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=35aZSQN27ck | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Sun_Learning_Rank-1_Diffractive_Optics_for_Single-Shot_High_Dynamic_Range_Imaging_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Sun_Learning_Rank-1_Diffractive_Optics_for_Single-Shot_High_Dynamic_Range_Imaging_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Sun_Learning_Rank-1_Diffractive_CVPR_2020_supplemental.pdf | null | null |
Universal Source-Free Domain Adaptation | Jogendra Nath Kundu, Naveen Venkat, Rahul M V, R. Venkatesh Babu | There is a strong incentive to develop versatile learning techniques that can transfer the knowledge of class-separability from a labeled source domain to an unlabeled target domain in the presence of a domain-shift. Existing domain adaptation (DA) approaches are not equipped for practical DA scenarios as a result of their reliance on the knowledge of source-target label-set relationship (e.g. Closed-set, Open-set or Partial DA). Furthermore, almost all prior unsupervised DA works require coexistence of source and target samples even during deployment, making them unsuitable for real-time adaptation. Devoid of such impractical assumptions, we propose a novel two-stage learning process. 1) In the Procurement stage, we aim to equip the model for future source-free deployment, assuming no prior knowledge of the upcoming category-gap and domain-shift. To achieve this, we enhance the model's ability to reject out-of-source distribution samples by leveraging the available source data, in a novel generative classifier framework. 2) In the Deployment stage, the goal is to design a unified adaptation algorithm capable of operating across a wide range of category-gaps, with no access to the previously seen source samples. To this end, in contrast to the usage of complex adversarial training regimes, we define a simple yet effective source-free adaptation objective by utilizing a novel instance-level weighting mechanism, named as Source Similarity Metric (SSM). A thorough evaluation shows the practical usability of the proposed learning framework with superior DA performance even over state-of-the-art source-dependent approaches. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kundu_Universal_Source-Free_Domain_Adaptation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.04393 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kundu_Universal_Source-Free_Domain_Adaptation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kundu_Universal_Source-Free_Domain_Adaptation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kundu_Universal_Source-Free_Domain_CVPR_2020_supplemental.pdf | null | null |
Meta-Transfer Learning for Zero-Shot Super-Resolution | Jae Woong Soh, Sunwoo Cho, Nam Ik Cho | Convolutional neural networks (CNNs) have shown dramatic improvements in single image super-resolution (SISR) by using large-scale external samples. Despite their remarkable performance based on the external dataset, they cannot exploit internal information within a specific image. Another problem is that they are applicable only to the specific condition of data that they are supervised. For instance, the low-resolution (LR) image should be a "bicubic" downsampled noise-free image from a high-resolution (HR) one. To address both issues, zero-shot super-resolution (ZSSR) has been proposed for flexible internal learning. However, they require thousands of gradient updates, i.e., long inference time. In this paper, we present Meta-Transfer Learning for Zero-Shot Super-Resolution (MZSR), which leverages ZSSR. Precisely, it is based on finding a generic initial parameter that is suitable for internal learning. Thus, we can exploit both external and internal information, where one single gradient update can yield quite considerable results. With our method, the network can quickly adapt to a given image condition. In this respect, our method can be applied to a large spectrum of image conditions within a fast adaptation process. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Soh_Meta-Transfer_Learning_for_Zero-Shot_Super-Resolution_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.12213 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Soh_Meta-Transfer_Learning_for_Zero-Shot_Super-Resolution_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Soh_Meta-Transfer_Learning_for_Zero-Shot_Super-Resolution_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Soh_Meta-Transfer_Learning_for_CVPR_2020_supplemental.pdf | null | null |
A Model-Driven Deep Neural Network for Single Image Rain Removal | Hong Wang, Qi Xie, Qian Zhao, Deyu Meng | Deep learning (DL) methods have achieved state-of-the-art performance in the task of single image rain removal. Most of current DL architectures, however, are still lack of sufficient interpretability and not fully integrated with physical structures inside general rain streaks. To this issue, in this paper, we propose a model-driven deep neural network for the task, with fully interpretable network structures. Specifically, based on the convolutional dictionary learning mechanism for representing rain, we propose a novel single image deraining model and utilize the proximal gradient descent technique to design an iterative algorithm only containing simple operators for solving the model. Such a simple implementation scheme facilitates us to unfold it into a new deep network architecture, called rain convolutional dictionary network (RCDNet), with almost every network module one-to-one corresponding to each operation involved in the algorithm. By end-to-end training the proposed RCDNet, all the rain kernels and proximal operators can be automatically extracted, faithfully characterizing the features of both rain and clean background layers, and thus naturally lead to its better deraining performance, especially in real scenarios. Comprehensive experiments substantiate the superiority of the proposed network, especially its well generality to diverse testing scenarios and good interpretability for all its modules, as compared with state-of-the-arts both visually and quantitatively. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_A_Model-Driven_Deep_Neural_Network_for_Single_Image_Rain_Removal_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.01333 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_A_Model-Driven_Deep_Neural_Network_for_Single_Image_Rain_Removal_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_A_Model-Driven_Deep_Neural_Network_for_Single_Image_Rain_Removal_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_A_Model-Driven_Deep_CVPR_2020_supplemental.pdf | null | null |
Predicting Lymph Node Metastasis Using Histopathological Images Based on Multiple Instance Learning With Deep Graph Convolution | Yu Zhao, Fan Yang, Yuqi Fang, Hailing Liu, Niyun Zhou, Jun Zhang, Jiarui Sun, Sen Yang, Bjoern Menze, Xinjuan Fan, Jianhua Yao | Multiple instance learning (MIL) is a typical weakly-supervised learning method where the label is associated with a bag of instances instead of a single instance. Despite extensive research over past years, effectively deploying MIL remains an open and challenging problem, especially when the commonly assumed standard multiple instance (SMI) assumption is not satisfied. In this paper, we propose a multiple instance learning method based on deep graph convolutional network and feature selection (FS-GCN-MIL) for histopathological image classification. The proposed method consists of three components, including instance-level feature extraction, instance-level feature selection, and bag-level classification. We develop a self-supervised learning mechanism to train the feature extractor based on a combination model of variational autoencoder and generative adversarial network (VAE-GAN). Additionally, we propose a novel instance-level feature selection method to select the discriminative instance features. Furthermore, we employ a graph convolutional network (GCN) for learning the bag-level representation and then performing the classification. We apply the proposed method in the prediction of lymph node metastasis using histopathological images of colorectal cancer. Experimental results demonstrate that the proposed method achieves superior performance compared to the state-of-the-art methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhao_Predicting_Lymph_Node_Metastasis_Using_Histopathological_Images_Based_on_Multiple_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=sfGDvAqoJwg | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Predicting_Lymph_Node_Metastasis_Using_Histopathological_Images_Based_on_Multiple_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Predicting_Lymph_Node_Metastasis_Using_Histopathological_Images_Based_on_Multiple_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
APQ: Joint Search for Network Architecture, Pruning and Quantization Policy | Tianzhe Wang, Kuan Wang, Han Cai, Ji Lin, Zhijian Liu, Hanrui Wang, Yujun Lin, Song Han | We present APQ, a novel design methodology for efficient deep learning deployment. Unlike previous methods that separately optimize the neural network architecture, pruning policy, and quantization policy, we design to optimize them in a joint manner. To deal with the larger design space it brings, we devise to train a quantization-aware accuracy predictor that is fed to the evolutionary search to select the best fit. Since directly training such a predictor requires time-consuming quantization data collection, we propose to use predictor-transfer technique to get the quantization-aware predictor: we first generate a large dataset of pairs by sampling a pretrained unified supernet and doing direct evaluation; then we use these data to train an accuracy predictor without quantization, further transferring its weights to train the quantization-aware predictor, which largely reduces the quantization data collection time. Extensive experiments on ImageNet show the benefits of this joint design methodology: the model searched by our method maintains the same level accuracy as ResNet34 8-bit model while saving 8x BitOps; we obtain the same level accuracy as MobileNetV2+HAQ while achieving 2x/1.3x latency/energy saving; the marginal search cost of joint optimization for a new deployment scenario outperforms separate optimizations using ProxylessNAS+AMC+HAQ by 2.3% accuracy while reducing 600x GPU hours and CO2 emission. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_APQ_Joint_Search_for_Network_Architecture_Pruning_and_Quantization_Policy_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.08509 | https://www.youtube.com/watch?v=fy1DUauWFaY | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_APQ_Joint_Search_for_Network_Architecture_Pruning_and_Quantization_Policy_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_APQ_Joint_Search_for_Network_Architecture_Pruning_and_Quantization_Policy_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_APQ_Joint_Search_CVPR_2020_supplemental.pdf | null | null |
SSRNet: Scalable 3D Surface Reconstruction Network | Zhenxing Mi, Yiming Luo, Wenbing Tao | Existing learning-based surface reconstruction methods from point clouds are still facing challenges in terms of scalability and preservation of details on large-scale point clouds. In this paper, we propose the SSRNet, a novel scalable learning-based method for surface reconstruction. The proposed SSRNet constructs local geometry-aware features for octree vertices and designs a scalable reconstruction pipeline, which not only greatly enhances the predication accuracy of the relative position between the vertices and the implicit surface facilitating the surface reconstruction quality, but also allows dividing the point cloud and octree vertices and processing different parts in parallel for superior scalability on large-scale point clouds with millions of points. Moreover, SSRNet demonstrates outstanding generalization capability and only needs several surface data for training, much less than other learning-based reconstruction methods, which can effectively avoid overfitting. The trained model of SSRNet on one dataset can be directly used on other datasets with superior performance. Finally, the time consumption with SSRNet on a large-scale point cloud is acceptable and competitive. To our knowledge, the proposed SSRNet is the first to really bring a convincing solution to the scalability issue of the learning-based surface reconstruction methods, and is an important step to make learning-based methods competitive with respect to geometry processing methods on real-world and challenging data. Experiments show that our method achieves a breakthrough in scalability and quality compared with state-of-the-art learning-based methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Mi_SSRNet_Scalable_3D_Surface_Reconstruction_Network_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.07401 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Mi_SSRNet_Scalable_3D_Surface_Reconstruction_Network_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Mi_SSRNet_Scalable_3D_Surface_Reconstruction_Network_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Mi_SSRNet_Scalable_3D_CVPR_2020_supplemental.zip | null | null |
Semantic Correspondence as an Optimal Transport Problem | Yanbin Liu, Linchao Zhu, Makoto Yamada, Yi Yang | Establishing dense correspondences across semantically similar images is a challenging task. Due to the large intra-class variation and background clutter, two common issues occur in current approaches. First, many pixels in a source image are assigned to one target pixel, i.e., many to one matching. Second, some object pixels are assigned to the background pixels, i.e., background matching. We solve the first issue by global feature matching, which maximizes the total matching correlations between images to obtain a global optimal matching matrix. The row sum and column sum constraints are enforced on the matching matrix to induce a balanced solution, thus suppressing the many to one matching. We solve the second issue by applying a staircase function on the class activation maps to re-weight the importance of pixels into four levels from foreground to background. The whole procedure is combined into a unified optimal transport algorithm by converting the maximization problem to the optimal transport formulation and incorporating the staircase weights into optimal transport algorithm to act as empirical distributions. The proposed algorithm achieves state-of-the-art performance on four benchmark datasets. Notably, a 26% relative improvement is achieved on the large-scale SPair-71k dataset. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Semantic_Correspondence_as_an_Optimal_Transport_Problem_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Semantic_Correspondence_as_an_Optimal_Transport_Problem_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Semantic_Correspondence_as_an_Optimal_Transport_Problem_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Liu_Semantic_Correspondence_as_CVPR_2020_supplemental.pdf | null | null |
Improving Confidence Estimates for Unfamiliar Examples | Zhizhong Li, Derek Hoiem | Intuitively, unfamiliarity should lead to lack of confidence. In reality, current algorithms often make highly confident yet wrong predictions when faced with relevant but unfamiliar examples. A classifier we trained to recognize gender is 12 times more likely to be wrong with a 99% confident prediction if presented with a subject from a different age group than those seen during training. In this paper, we compare and evaluate several methods to improve confidence estimates for unfamiliar and familiar samples. We propose a testing methodology of splitting unfamiliar and familiar samples by attribute (age, breed, subcategory) or sampling (similar datasets collected by different people at different times). We evaluate methods including confidence calibration, ensembles, distillation, and a Bayesian model and use several metrics to analyze label, likelihood, and calibration error. While all methods reduce over-confident errors, the ensemble of calibrated models performs best overall, and T-scaling performs best among the approaches with fastest inference. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Improving_Confidence_Estimates_for_Unfamiliar_Examples_CVPR_2020_paper.pdf | http://arxiv.org/abs/1804.03166 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Improving_Confidence_Estimates_for_Unfamiliar_Examples_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Improving_Confidence_Estimates_for_Unfamiliar_Examples_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Improving_Confidence_Estimates_CVPR_2020_supplemental.pdf | null | null |
Learning Generative Models of Shape Handles | Matheus Gadelha, Giorgio Gori, Duygu Ceylan, Radomir Mech, Nathan Carr, Tamy Boubekeur, Rui Wang, Subhransu Maji | We present a generative model to synthesize 3D shapes as sets of handles -- lightweight proxies that approximate the original 3D shape -- for applications in interactive editing, shape parsing, and building compact 3D representations. Our model can generate handle sets with varying cardinality and different types of handles. Key to our approach is a deep architecture that predicts both the parameters and existence of shape handles and a novel similarity measure that can easily accommodate different types of handles, such as cuboids or sphere-meshes. We leverage the recent advances in semantic 3D annotation as well as automatic shape summarization techniques to supervise our approach. We show that the resulting shape representations are not only intuitive, but achieve superior quality than previous state-of-the-art. Finally, we demonstrate how our method can be used in applications such as interactive shape editing and completion, leveraging the latent space learned by our model to guide these tasks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gadelha_Learning_Generative_Models_of_Shape_Handles_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.03028 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gadelha_Learning_Generative_Models_of_Shape_Handles_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gadelha_Learning_Generative_Models_of_Shape_Handles_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Gadelha_Learning_Generative_Models_CVPR_2020_supplemental.pdf | null | null |
Toward a Universal Model for Shape From Texture | Dor Verbin, Todd Zickler | We consider the shape from texture problem, where the input is a single image of a curved, textured surface, and the texture and shape are both a priori unknown. We formulate this task as a three-player game between a shape process, a texture process, and a discriminator. The discriminator adapts a set of non-linear filters to try to distinguish image patches created by the texture process from those created by the shape process, while the shape and texture processes try to create image patches that are indistinguishable from those of the other. An equilibrium of this game yields two things: an estimate of the 2.5D surface from the shape process, and a stochastic texture synthesis model from the texture process. Experiments show that this approach is robust to common non-idealities such as shading, gloss, and clutter. We also find that it succeeds for a wide variety of texture types, including both periodic textures and those composed of isolated textons, which have previously required distinct and specialized processing. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Verbin_Toward_a_Universal_Model_for_Shape_From_Texture_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Verbin_Toward_a_Universal_Model_for_Shape_From_Texture_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Verbin_Toward_a_Universal_Model_for_Shape_From_Texture_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Verbin_Toward_a_Universal_CVPR_2020_supplemental.pdf | null | null |
Stochastic Sparse Subspace Clustering | Ying Chen, Chun-Guang Li, Chong You | State-of-the-art subspace clustering methods are based on self-expressive model, which represents each data point as a linear combination of other data points. By enforcing such representation to be sparse, sparse subspace clustering is guaranteed to produce a subspace-preserving data affinity where two points are connected only if they are from the same subspace. On the other hand, however, data points from the same subspace may not be well-connected, leading to the issue of over-segmentation. We introduce dropout to address the issue of over-segmentation, which is based on randomly dropping out data points in self-expressive model. In particular, we show that dropout is equivalent to adding a squared l_2 norm regularization on the representation coefficients, therefore induces denser solutions. Then, we reformulate the optimization problem as a consensus problem over a set of small-scale subproblems. This leads to a scalable and flexible sparse subspace clustering approach, termed Stochastic Sparse Subspace Clustering, which can effectively handle large scale datasets. Extensive experiments on synthetic data and real world datasets validate the efficiency and effectiveness of our proposal. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Stochastic_Sparse_Subspace_Clustering_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.01449 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Stochastic_Sparse_Subspace_Clustering_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Stochastic_Sparse_Subspace_Clustering_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learn2Perturb: An End-to-End Feature Perturbation Learning to Improve Adversarial Robustness | Ahmadreza Jeddi, Mohammad Javad Shafiee, Michelle Karg, Christian Scharfenberger, Alexander Wong | While deep neural networks have been achieving state-of-the-art performance across a wide variety of applications, their vulnerability to adversarial attacks limits their widespread deployment for safety-critical applications. Alongside other adversarial defense approaches being investigated, there has been a very recent interest in improving adversarial robustness in deep neural networks through the introduction of perturbations during the training process. However, such methods leverage fixed, pre-defined perturbations and require significant hyper-parameter tuning that makes them very difficult to leverage in a general fashion. In this study, we introduce Learn2Perturb, an end-to-end feature perturbation learning approach for improving the adversarial robustness of deep neural networks. More specifically, we introduce novel perturbation-injection modules that are incorporated at each layer to perturb the feature space and increase uncertainty in the network. This feature perturbation is performed at both the training and the inference stages. Furthermore, inspired by the Expectation-Maximization, an alternating back-propagation training algorithm is introduced to train the network and noise parameters consecutively. Experimental results on CIFAR-10 and CIFAR-100 datasets show that the proposed Learn2Perturb method can result in deep neural networks which are 4-7% more robust on l_inf FGSM and PDG adversarial attacks and significantly outperforms the state-of-the-art against l_2 C&W attack and a wide range of well-known black-box attacks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jeddi_Learn2Perturb_An_End-to-End_Feature_Perturbation_Learning_to_Improve_Adversarial_Robustness_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.01090 | https://www.youtube.com/watch?v=-XYfnsmadDs | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jeddi_Learn2Perturb_An_End-to-End_Feature_Perturbation_Learning_to_Improve_Adversarial_Robustness_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jeddi_Learn2Perturb_An_End-to-End_Feature_Perturbation_Learning_to_Improve_Adversarial_Robustness_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Jeddi_Learn2Perturb_An_End-to-End_CVPR_2020_supplemental.pdf | null | null |
Syn2Real Transfer Learning for Image Deraining Using Gaussian Processes | Rajeev Yasarla, Vishwanath A. Sindagi, Vishal M. Patel | Recent CNN-based methods for image deraining have achieved excellent performance in terms of reconstruction error as well as visual quality. However, these methods are limited in the sense that they can be trained only on fully labeled data. Due to various challenges in obtaining real world fully-labeled image deraining datasets, existing methods are trained only on synthetically generated data and hence, generalize poorly to real-world images. The use of real-world data in training image deraining networks is relatively less explored in the literature. We propose a Gaussian Process-based semi-supervised learning framework which enables the network in learning to derain using synthetic dataset while generalizing better using unlabeled real-world images. Through extensive experiments and ablations on several challenging datasets (such as Rain800, Rain100H and DDN-SIRR), we show that the proposed method, when trained on limited labeled data, achieves on-par performance with fully-labeled training. Additionally, we demonstrate that using unlabeled real-world images in the proposed GP-based framework results in superior performance as compared to existing methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yasarla_Syn2Real_Transfer_Learning_for_Image_Deraining_Using_Gaussian_Processes_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.05580 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yasarla_Syn2Real_Transfer_Learning_for_Image_Deraining_Using_Gaussian_Processes_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yasarla_Syn2Real_Transfer_Learning_for_Image_Deraining_Using_Gaussian_Processes_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yasarla_Syn2Real_Transfer_Learning_CVPR_2020_supplemental.pdf | null | null |
On Isometry Robustness of Deep 3D Point Cloud Models Under Adversarial Attacks | Yue Zhao, Yuwei Wu, Caihua Chen, Andrew Lim | While deep learning in 3D domain has achieved revolutionary performance in many tasks, the robustness of these models has not been sufficiently studied or explored. Regarding the 3D adversarial samples, most existing works focus on manipulation of local points, which may fail to invoke the global geometry properties, like robustness under linear projection that preserves the Euclidean distance, i.e., isometry. In this work, we show that existing state-of-the-art deep 3D models are extremely vulnerable to isometry transformations. Armed with the Thompson Sampling, we develop a black-box attack with success rate over 95% on ModelNet40 data set. Incorporating with the Restricted Isometry Property, we propose a novel framework of white-box attack on top of spectral norm based perturbation. In contrast to previous works, our adversarial samples are experimentally shown to be strongly transferable. Evaluated on a sequence of prevailing 3D models, our white-box attack achieves success rates from 98.88% to 100%. It maintains a successful attack rate over 95% even within an imperceptible rotation range [+-2.81*]. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhao_On_Isometry_Robustness_of_Deep_3D_Point_Cloud_Models_Under_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.12222 | https://www.youtube.com/watch?v=nY5fJxxzdRg | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_On_Isometry_Robustness_of_Deep_3D_Point_Cloud_Models_Under_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_On_Isometry_Robustness_of_Deep_3D_Point_Cloud_Models_Under_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhao_On_Isometry_Robustness_CVPR_2020_supplemental.zip | null | null |
Self-Supervised Viewpoint Learning From Image Collections | Siva Karthik Mustikovela, Varun Jampani, Shalini De Mello, Sifei Liu, Umar Iqbal, Carsten Rother, Jan Kautz | Training deep neural networks to estimate the viewpoint of objects requires large labeled training datasets. However, manually labeling viewpoints is notoriously hard, error-prone, and time-consuming. On the other hand, it is relatively easy to mine many unlabeled images of an object category from the internet, e.g., of cars or faces. We seek to answer the research question of whether such unlabeled collections of in-the-wild images can be successfully utilized to train viewpoint estimation networks for general object categories purely via self-supervision. Self-supervision here refers to the fact that the only true supervisory signal that the network has is the input image itself. We propose a novel learning framework which incorporates an analysis-by-synthesis paradigm to reconstruct images in a viewpoint aware manner with a generative network, along with symmetry and adversarial constraints to successfully supervise our viewpoint estimation network. We show that our approach performs competitively to fully-supervised approaches for several object categories like human faces, cars, buses, and trains. Our work opens up further research in self-supervised viewpoint learning and serves as a robust baseline for it. We open-source our code at https://github.com/NVlabs/SSV. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Mustikovela_Self-Supervised_Viewpoint_Learning_From_Image_Collections_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.01793 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Mustikovela_Self-Supervised_Viewpoint_Learning_From_Image_Collections_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Mustikovela_Self-Supervised_Viewpoint_Learning_From_Image_Collections_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Mustikovela_Self-Supervised_Viewpoint_Learning_CVPR_2020_supplemental.zip | null | null |
On the Uncertainty of Self-Supervised Monocular Depth Estimation | Matteo Poggi, Filippo Aleotti, Fabio Tosi, Stefano Mattoccia | Self-supervised paradigms for monocular depth estimation are very appealing since they do not require ground truth annotations at all. Despite the astonishing results yielded by such methodologies, learning to reason about the uncertainty of the estimated depth maps is of paramount importance for practical applications, yet uncharted in the literature. Purposely, we explore for the first time how to estimate the uncertainty for this task and how this affects depth accuracy, proposing a novel peculiar technique specifically designed for self-supervised approaches. On the standard KITTI dataset, we exhaustively assess the performance of each method with different self-supervised paradigms. Such evaluation highlights that our proposal i) always improves depth accuracy significantly and ii) yields state-of-the-art results concerning uncertainty estimation when training on sequences and competitive results uniquely deploying stereo pairs. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Poggi_On_the_Uncertainty_of_Self-Supervised_Monocular_Depth_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.06209 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Poggi_On_the_Uncertainty_of_Self-Supervised_Monocular_Depth_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Poggi_On_the_Uncertainty_of_Self-Supervised_Monocular_Depth_Estimation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Poggi_On_the_Uncertainty_CVPR_2020_supplemental.pdf | null | null |
StegaStamp: Invisible Hyperlinks in Physical Photographs | Matthew Tancik, Ben Mildenhall, Ren Ng | Printed and digitally displayed photos have the ability to hide imperceptible digital data that can be accessed through internet-connected imaging systems. Another way to think about this is physical photographs that have unique QR codes invisibly embedded within them. This paper presents an architecture, algorithms, and a prototype implementation addressing this vision. Our key technical contribution is StegaStamp, a learned steganographic algorithm to enable robust encoding and decoding of arbitrary hyperlink bitstrings into photos in a manner that approaches perceptual invisibility. StegaStamp comprises a deep neural network that learns an encoding/decoding algorithm robust to image perturbations approximating the space of distortions resulting from real printing and photography. We demonstrates real-time decoding of hyperlinks in photos from in-the-wild videos that contain variation in lighting, shadows, perspective, occlusion and viewing distance. Our prototype system robustly retrieves 56 bit hyperlinks after error correction -- sufficient to embed a unique code within every photo on the internet. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tancik_StegaStamp_Invisible_Hyperlinks_in_Physical_Photographs_CVPR_2020_paper.pdf | http://arxiv.org/abs/1904.05343 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tancik_StegaStamp_Invisible_Hyperlinks_in_Physical_Photographs_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tancik_StegaStamp_Invisible_Hyperlinks_in_Physical_Photographs_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Tancik_StegaStamp_Invisible_Hyperlinks_CVPR_2020_supplemental.pdf | null | null |
Anisotropic Convolutional Networks for 3D Semantic Scene Completion | Jie Li, Kai Han, Peng Wang, Yu Liu, Xia Yuan | As a voxel-wise labeling task, semantic scene completion (SSC) tries to simultaneously infer the occupancy and semantic labels for a scene from a single depth and/or RGB image. The key challenge for SSC is how to effectively take advantage of the 3D context to model various objects or stuffs with severe variations in shapes, layouts, and visibility. To handle such variations, we propose a novel module called anisotropic convolution, which properties with flexibility and power impossible for the competing methods such as standard 3D convolution and some of its variations. In contrast to the standard 3D convolution that is limited to a fixed 3D receptive field, our module is capable of modeling the dimensional anisotropy voxel-wisely. The basic idea is to enable anisotropic 3D receptive field by decomposing a 3D convolution into three consecutive 1D convolutions, and the kernel size for each such 1D convolution is adaptively determined on the fly. By stacking multiple such anisotropic convolution modules, the voxel-wise modeling capability can be further enhanced while maintaining a controllable amount of model parameters. Extensive experiments on two SSC benchmarks, NYU-Depth-v2 and NYUCAD, show the superior performance of the proposed method. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Anisotropic_Convolutional_Networks_for_3D_Semantic_Scene_Completion_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.02122 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Anisotropic_Convolutional_Networks_for_3D_Semantic_Scene_Completion_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Anisotropic_Convolutional_Networks_for_3D_Semantic_Scene_Completion_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning to Have an Ear for Face Super-Resolution | Givi Meishvili, Simon Jenni, Paolo Favaro | We propose a novel method to use both audio and a low-resolution image to perform extreme face super-resolution (a 16x increase of the input size). When the resolution of the input image is very low (e.g., 8x8 pixels), the loss of information is so dire that important details of the original identity have been lost and audio can aid the recovery of a plausible high-resolution image. In fact, audio carries information about facial attributes, such as gender and age. To combine the aural and visual modalities, we propose a method to first build the latent representations of a face from the lone audio track and then from the lone low-resolution image. We then train a network to fuse these two representations. We show experimentally that audio can assist in recovering attributes such as the gender, the age and the identity, and thus improve the correctness of the high-resolution image reconstruction process. Our procedure does not make use of human annotation and thus can be easily trained with existing video datasets. Moreover, we show that our model builds a factorized representation of images and audio as it allows one to mix low-resolution images and audio from different videos and to generate realistic faces with semantically meaningful combinations. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Meishvili_Learning_to_Have_an_Ear_for_Face_Super-Resolution_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.12780 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Meishvili_Learning_to_Have_an_Ear_for_Face_Super-Resolution_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Meishvili_Learning_to_Have_an_Ear_for_Face_Super-Resolution_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Meishvili_Learning_to_Have_CVPR_2020_supplemental.pdf | null | null |
Cascaded Refinement Network for Point Cloud Completion | Xiaogang Wang, Marcelo H. Ang Jr., Gim Hee Lee | Point clouds are often sparse and incomplete. Existing shape completion methods are incapable of generating details of objects or learning the complex point distributions. To this end, we propose a cascaded refinement network together with a coarse-to-fine strategy to synthesize the detailed object shapes. Considering the local details of partial input with the global shape information together, we can preserve the existing details in the incomplete point set and generate the missing parts with high fidelity. We also design a patch discriminator that guarantees every local area has the same pattern with the ground truth to learn the complicated point distribution. Quantitative and qualitative experiments on different datasets show that our method achieves superior results compared to existing state-of-the-art approaches on the 3D point cloud completion task. Our source code is available at https://github.com/xiaogangw/cascaded-point-completion.git. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Cascaded_Refinement_Network_for_Point_Cloud_Completion_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.03327v3 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Cascaded_Refinement_Network_for_Point_Cloud_Completion_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Cascaded_Refinement_Network_for_Point_Cloud_Completion_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_Cascaded_Refinement_Network_CVPR_2020_supplemental.pdf | null | null |
DOA-GAN: Dual-Order Attentive Generative Adversarial Network for Image Copy-Move Forgery Detection and Localization | Ashraful Islam, Chengjiang Long, Arslan Basharat, Anthony Hoogs | Images can be manipulated for nefarious purposes to hide content or to duplicate certain objects through copy-move operations. Discovering a well-crafted copy-move forgery in images can be very challenging for both humans and machines; for example, an object on a uniform background can be replaced by an image patch of the same background. In this paper, we propose a Generative Adversarial Network with a dual-order attention model to detect and localize copy-move forgeries. In the generator, the first-order attention is designed to capture copy-move location information, and the second-order attention exploits more discriminative features for the patch co-occurrence. Both attention maps are extracted from the affinity matrix and are used to fuse location-aware and co-occurrence features for the final detection and localization branches of the network. The discriminator network is designed to further ensure more accurate localization results. To the best of our knowledge, we are the first to propose such a network architecture with the 1st-order attention mechanism from the affinity matrix. We have performed extensive experimental validation and our state-of-the-art results strongly demonstrate the efficacy of the proposed approach. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Islam_DOA-GAN_Dual-Order_Attentive_Generative_Adversarial_Network_for_Image_Copy-Move_Forgery_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=ezmYkNblTP4 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Islam_DOA-GAN_Dual-Order_Attentive_Generative_Adversarial_Network_for_Image_Copy-Move_Forgery_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Islam_DOA-GAN_Dual-Order_Attentive_Generative_Adversarial_Network_for_Image_Copy-Move_Forgery_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Islam_DOA-GAN_Dual-Order_Attentive_CVPR_2020_supplemental.pdf | null | null |
Rotation Equivariant Graph Convolutional Network for Spherical Image Classification | Qin Yang, Chenglin Li, Wenrui Dai, Junni Zou, Guo-Jun Qi, Hongkai Xiong | Convolutional neural networks (CNNs) designed for low-dimensional regular grids will unfortunately lead to non-optimal solutions for analyzing spherical images, due to their different geometrical properties from planar images. In this paper, we generalize the grid-based CNNs to a non-Euclidean space by taking into account the geometry of spherical surfaces and propose a Spherical Graph Convolutional Network (SGCN) to encode rotation equivariant representations. Specifically, we propose a spherical graph construction criterion showing that a graph needs to be regular by evenly covering the spherical surfaces in order to design a rotation equivariant graph convolutional layer. For the practical case where the perfectly regular graph does not exist, we design two quantitative measures to evaluate the degree of irregularity for a spherical graph. The Geodesic ICOsahedral Pixelation (GICOPix) is adopted to construct spherical graphs with the minimum degree of irregularity compared to the current popular pixelation schemes. In addition, we design a hierarchical pooling layer to keep the rotation-equivariance, followed by a transition layer to enforce the invariance to the rotations for spherical image classification. We evaluate the proposed graph convolutional layers with different pixelations schemes in terms of equivariance errors. We also assess the effectiveness of the proposed SGCN in fulfilling rotation-invariance by the invariance error of the transition layers and recognizing the spherical images and 3D objects. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_Rotation_Equivariant_Graph_Convolutional_Network_for_Spherical_Image_Classification_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=E9KhudotR54 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Rotation_Equivariant_Graph_Convolutional_Network_for_Spherical_Image_Classification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Rotation_Equivariant_Graph_Convolutional_Network_for_Spherical_Image_Classification_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Composing Good Shots by Exploiting Mutual Relations | Debang Li, Junge Zhang, Kaiqi Huang, Ming-Hsuan Yang | Finding views with a good composition from an input image is a common but challenging problem. There are usually at least dozens of candidates (regions) in an image, and how to evaluate these candidates is subjective. Most existing methods only use the feature corresponding to each candidate to evaluate the quality. However, the mutual relations between the candidates from an image play an essential role in composing a good shot due to the comparative nature of this problem. Motivated by this, we propose a graph-based module with a gated feature update to model the relations between different candidates. The candidate region features are propagated on a graph that models mutual relations between different regions for mining the useful information such that the relation features and region features are adaptively fused. We design a multi-task loss to train the model, especially, a regularization term is adopted to incorporate the prior knowledge about the relations into the graph. A data augmentation method is also developed by mixing nodes from different graphs to improve the model generalization ability. Experimental results show that the proposed model performs favorably against state-of-the-art methods, and comprehensive ablation studies demonstrate the contribution of each module and graph-based inference of the proposed method. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Composing_Good_Shots_by_Exploiting_Mutual_Relations_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Composing_Good_Shots_by_Exploiting_Mutual_Relations_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Composing_Good_Shots_by_Exploiting_Mutual_Relations_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Composing_Good_Shots_CVPR_2020_supplemental.pdf | null | null |
DIST: Rendering Deep Implicit Signed Distance Function With Differentiable Sphere Tracing | Shaohui Liu, Yinda Zhang, Songyou Peng, Boxin Shi, Marc Pollefeys, Zhaopeng Cui | We propose a differentiable sphere tracing algorithm to bridge the gap between inverse graphics methods and the recently proposed deep learning based implicit signed distance function. Due to the nature of the implicit function, the rendering process requires tremendous function queries, which is particularly problematic when the function is represented as a neural network. We optimize both the forward and backward pass of our rendering layer to make it run efficiently with affordable memory consumption on a commodity graphics card. Our rendering method is fully differentiable such that losses can be directly computed on the rendered 2D observations, and the gradients can be propagated backward to optimize the 3D geometry. We show that our rendering method can effectively reconstruct accurate 3D shapes from various inputs, such as sparse depth and multi-view images, through inverse optimization. With the geometry based reasoning, our 3D shape prediction methods show excellent generalization capability and robustness against various noises. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_DIST_Rendering_Deep_Implicit_Signed_Distance_Function_With_Differentiable_Sphere_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.13225 | https://www.youtube.com/watch?v=_TAlKknAqyI | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_DIST_Rendering_Deep_Implicit_Signed_Distance_Function_With_Differentiable_Sphere_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_DIST_Rendering_Deep_Implicit_Signed_Distance_Function_With_Differentiable_Sphere_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Liu_DIST_Rendering_Deep_CVPR_2020_supplemental.zip | null | null |
Progressive Relation Learning for Group Activity Recognition | Guyue Hu, Bo Cui, Yuan He, Shan Yu | Group activities usually involve spatio-temporal dynamics among many interactive individuals, while only a few participants at several key frames essentially define the activity. Therefore, effectively modeling the group-relevant and suppressing the irrelevant actions (and interactions) are vital for group activity recognition. In this paper, we propose a novel method based on deep reinforcement learning to progressively refine the low-level features and high-level relations of group activities. Firstly, we construct a semantic relation graph (SRG) to explicitly model the relations among persons. Then, two agents adopting policy according to two Markov decision processes are applied to progressively refine the SRG. Specifically, one feature-distilling (FD) agent in the discrete action space refines the low-level spatio-temporal features by distilling the most informative frames. Another relation-gating (RG) agent in continuous action space adjusts the high-level semantic graph to pay more attention to group-relevant relations. The SRG, FD agent, and RG agent are optimized alternately to mutually boost the performance of each other. Extensive experiments on two widely used benchmarks demonstrate the effectiveness and superiority of the proposed approach. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hu_Progressive_Relation_Learning_for_Group_Activity_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/1908.02948 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hu_Progressive_Relation_Learning_for_Group_Activity_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hu_Progressive_Relation_Learning_for_Group_Activity_Recognition_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Hu_Progressive_Relation_Learning_CVPR_2020_supplemental.pdf | null | null |
Boundary-Aware 3D Building Reconstruction From a Single Overhead Image | Jisan Mahmud, True Price, Akash Bapat, Jan-Michael Frahm | We propose a boundary-aware multi-task deep-learning-based framework for fast 3D building modeling from a single overhead image. Unlike most existing techniques which rely on multiple images for 3D scene modeling, we seek to model the buildings in the scene from a single overhead image by jointly learning a modified signed distance function (SDF) from the building boundaries, a dense heightmap of the scene, and scene semantics. To jointly train for these tasks, we leverage pixel-wise semantic segmentation and normalized digital surface maps (nDSM) as supervision, in addition to labeled building outlines. At test time, buildings in the scene are automatically modeled in 3D using only an input overhead image. We demonstrate an increase in building modeling performance using a multi-feature network architecture that improves building outline detection by considering network features learned for the other jointly learned tasks. We also introduce a novel mechanism for robustly refining instance-specific building outlines using the learned modified SDF. We verify the effectiveness of our method on multiple large-scale satellite and aerial imagery datasets, where we obtain state-of-the-art performance in the 3D building reconstruction task. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Mahmud_Boundary-Aware_3D_Building_Reconstruction_From_a_Single_Overhead_Image_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Mahmud_Boundary-Aware_3D_Building_Reconstruction_From_a_Single_Overhead_Image_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Mahmud_Boundary-Aware_3D_Building_Reconstruction_From_a_Single_Overhead_Image_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Mahmud_Boundary-Aware_3D_Building_CVPR_2020_supplemental.pdf | null | null |
Learning Formation of Physically-Based Face Attributes | Ruilong Li, Karl Bladin, Yajie Zhao, Chinmay Chinara, Owen Ingraham, Pengda Xiang, Xinglei Ren, Pratusha Prasad, Bipin Kishore, Jun Xing, Hao Li | Based on a combined data set of 4000 high resolution facial scans, we introduce a non-linear morphable face model, capable of producing multifarious face geometry of pore-level resolution, coupled with material attributes for use in physically-based rendering. We aim to maximize the variety of the participant's face identities, while increasing the robustness of correspondence between unique components, including middle-frequency geometry, albedo maps, specular intensity maps and high-frequency displacement details. Our deep learning based generative model learns to correlate albedo and geometry, which ensures the anatomical correctness of the generated assets. We demonstrate potential use of our generative model for novel identity generation, model fitting, interpolation, animation, high fidelity data visualization, and low-to-high resolution data domain transferring. We hope the release of this generative model will encourage further cooperation between all graphics, vision, and data focused professionals, while demonstrating the cumulative value of every individual's complete biometric profile. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Learning_Formation_of_Physically-Based_Face_Attributes_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.03458 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Learning_Formation_of_Physically-Based_Face_Attributes_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Learning_Formation_of_Physically-Based_Face_Attributes_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Learning_Formation_of_CVPR_2020_supplemental.zip | null | null |
Deep Metric Learning via Adaptive Learnable Assessment | Wenzhao Zheng, Jiwen Lu, Jie Zhou | In this paper, we propose a deep metric learning via adaptive learnable assessment (DML-ALA) method for image retrieval and clustering, which aims to learn a sample assessment strategy to maximize the generalization of the trained metric. Unlike existing deep metric learning methods that usually utilize a fixed sampling strategy like hard negative mining, we propose a sequence-aware learnable assessor which re-weights each training example to train the metric towards good generalization. We formulate the learning of this assessor as a meta-learning problem, where we employ an episode-based training scheme and update the assessor at each iteration to adapt to the current model status. We construct each episode by sampling two subsets of disjoint labels to simulate the procedure of training and testing and use the performance of one-gradient-updated metric on the validation subset as the meta-objective of the assessor. Experimental results on the widely used CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate the effectiveness of the proposed approach. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zheng_Deep_Metric_Learning_via_Adaptive_Learnable_Assessment_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Deep_Metric_Learning_via_Adaptive_Learnable_Assessment_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Deep_Metric_Learning_via_Adaptive_Learnable_Assessment_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Rethinking Computer-Aided Tuberculosis Diagnosis | Yun Liu, Yu-Huan Wu, Yunfeng Ban, Huifang Wang, Ming-Ming Cheng | As a serious infectious disease, tuberculosis (TB) is one of the major threats to human health worldwide, leading to millions of death every year. Although early diagnosis and treatment can greatly improve the chances of survival, it remains a major challenge, especially in developing countries. Computer-aided tuberculosis diagnosis (CTD) is a promising choice for TB diagnosis due to the great successes of deep learning. However, when it comes to TB diagnosis, the lack of training data has hampered the progress of CTD. To solve this problem, we establish a large-scale TB dataset, namely Tuberculosis X-ray (TBX11K) dataset. This dataset contains 11200 X-ray images with corresponding bounding box annotations for TB areas, while the existing largest public TB dataset only has 662 X-ray images with corresponding image-level annotations. The proposed dataset enables the training of sophisticated detectors for high-quality CTD. We reform the existing object detectors to adapt them to simultaneous image classification and TB area detection. These reformed detectors are trained and evaluated on the proposed TBX11K dataset and served as the baselines for future research. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Rethinking_Computer-Aided_Tuberculosis_Diagnosis_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Rethinking_Computer-Aided_Tuberculosis_Diagnosis_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Rethinking_Computer-Aided_Tuberculosis_Diagnosis_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Creating Something From Nothing: Unsupervised Knowledge Distillation for Cross-Modal Hashing | Hengtong Hu, Lingxi Xie, Richang Hong, Qi Tian | In recent years, cross-modal hashing (CMH) has attracted increasing attentions, mainly because its potential ability of mapping contents from different modalities, especially in vision and language, into the same space, so that it becomes efficient in cross-modal data retrieval. There are two main frameworks for CMH, differing from each other in whether semantic supervision is required. Compared to the unsupervised methods, the supervised methods often enjoy more accurate results, but require much heavier labors in data annotation. In this paper, we propose a novel approach that enables guiding a supervised method using outputs produced by an unsupervised method. Specifically, we make use of teacher-student optimization for propagating knowledge. Experiments are performed on two popular CMH benchmarks, i.e., the MIRFlickr and NUS-WIDE datasets. Our approach outperforms all existing unsupervised methods by a large margin. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hu_Creating_Something_From_Nothing_Unsupervised_Knowledge_Distillation_for_Cross-Modal_Hashing_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00280 | https://www.youtube.com/watch?v=Io1uloVOEJk | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hu_Creating_Something_From_Nothing_Unsupervised_Knowledge_Distillation_for_Cross-Modal_Hashing_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hu_Creating_Something_From_Nothing_Unsupervised_Knowledge_Distillation_for_Cross-Modal_Hashing_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Adversarial Examples Improve Image Recognition | Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan L. Yuille, Quoc V. Le | Adversarial examples are commonly viewed as a threat to ConvNets. Here we present an opposite perspective: adversarial examples can be used to improve image recognition models if harnessed in the right manner. We propose AdvProp, an enhanced adversarial training scheme which treats adversarial examples as additional examples, to prevent overfitting. Key to our method is the usage of a separate auxiliary batch norm for adversarial examples, as they have different underlying distributions to normal examples. We show that AdvProp improves a wide range of models on various image recognition tasks and performs better when the models are bigger. For instance, by applying AdvProp to the latest EfficientNet-B7 [28] on ImageNet, we achieve significant improvements on ImageNet (+0.7%), ImageNet-C (+6.5%), ImageNet-A (+7.0%), Stylized-ImageNet (+4.8%). With an enhanced EfficientNet-B8, our method achieves the state-of-the-art 85.5% ImageNet top-1 accuracy without extra data. This result even surpasses the best model in [20] which is trained with 3.5B Instagram images ( 3000X more than ImageNet) and 9.4X more parameters. Code and models will be made publicly available. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xie_Adversarial_Examples_Improve_Image_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.09665 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xie_Adversarial_Examples_Improve_Image_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xie_Adversarial_Examples_Improve_Image_Recognition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution | Yapeng Tian, Yulun Zhang, Yun Fu, Chenliang Xu | Video super-resolution (VSR) aims to restore a photo-realistic high-resolution (HR) video frame from both its corresponding low-resolution (LR) frame (reference frame) and multiple neighboring frames (supporting frames). Due to varying motion of cameras or objects, the reference frame and each support frame are not aligned. Therefore, temporal alignment is a challenging yet important problem for VSR. Previous VSR methods usually utilize optical flow between the reference frame and each supporting frame to warp the supporting frame for temporal alignment. However, both inaccurate flow and the image-level warping strategy will lead to artifacts in the warped supporting frames. To overcome the limitation, we propose a temporally-deformable alignment network (TDAN) to adaptively align the reference frame and each supporting frame at the feature level without computing optical flow. The TDAN uses features from both the reference frame and each supporting frame to dynamically predict offsets of sampling convolution kernels. By using the corresponding kernels, TDAN transforms supporting frames to align with the reference frame. To predict the HR video frame, a reconstruction network taking aligned frames and the reference frame is utilized. Experimental results demonstrate that the TDAN is capable of alleviating occlusions and artifacts for temporal alignment and the TDAN-based VSR model outperforms several recent state-of-the-art VSR networks with a comparable or even much smaller model size. The source code and pre-trained models are released in https://github.com/YapengTian/TDAN-VSR. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tian_TDAN_Temporally-Deformable_Alignment_Network_for_Video_Super-Resolution_CVPR_2020_paper.pdf | http://arxiv.org/abs/1812.02898 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tian_TDAN_Temporally-Deformable_Alignment_Network_for_Video_Super-Resolution_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tian_TDAN_Temporally-Deformable_Alignment_Network_for_Video_Super-Resolution_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Retina-Like Visual Image Reconstruction via Spiking Neural Model | Lin Zhu, Siwei Dong, Jianing Li, Tiejun Huang, Yonghong Tian | The high-sensitivity vision of primates, including humans, is mediated by a small retinal region called the fovea. As a novel bio-inspired vision sensor, spike camera mimics the fovea to record the nature scenes by continuous-time spikes instead of frame-based manner. However, reconstructing visual images from the spikes remains to be a challenge. In this paper, we design a retina-like visual image reconstruction framework, which is flexible in reconstructing full texture of natural scenes from the totally new spike data. Specifically, the proposed architecture consists of motion local excitation layer, spike refining layer and visual reconstruction layer motivated by bio-realistic leaky integrate and fire (LIF) neurons and synapse connection with spike-timing-dependent plasticity (STDP) rules. This approach may represent a major shift from conventional frame-based vision to the continuous-time retina-like vision, owning to the advantages of high temporal resolution and low power consumption. To test the performance, a spike dataset is constructed which is recorded by the spike camera. The experimental results show that the proposed approach is extremely effective in reconstructing the visual image in both normal and high speed scenes, while achieving high dynamic range and high image quality. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhu_Retina-Like_Visual_Image_Reconstruction_via_Spiking_Neural_Model_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_Retina-Like_Visual_Image_Reconstruction_via_Spiking_Neural_Model_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_Retina-Like_Visual_Image_Reconstruction_via_Spiking_Neural_Model_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhu_Retina-Like_Visual_Image_CVPR_2020_supplemental.zip | null | null |
Resolution Adaptive Networks for Efficient Inference | Le Yang, Yizeng Han, Xi Chen, Shiji Song, Jifeng Dai, Gao Huang | Adaptive inference is an effective mechanism to achieve a dynamic tradeoff between accuracy and computational cost in deep networks. Existing works mainly exploit architecture redundancy in network depth or width. In this paper, we focus on spatial redundancy of input samples and propose a novel Resolution Adaptive Network (RANet), which is inspired by the intuition that low-resolution representations are sufficient for classifying "easy" inputs containing large objects with prototypical features, while only some "hard" samples need spatially detailed information. In RANet, the input images are first routed to a lightweight sub-network that efficiently extracts low-resolution representations, and those samples with high prediction confidence will exit early from the network without being further processed. Meanwhile, high-resolution paths in the network maintain the capability to recognize the "hard" samples. Therefore, RANet can effectively reduce the spatial redundancy involved in inferring high-resolution inputs. Empirically, we demonstrate the effectiveness of the proposed RANet on the CIFAR-10, CIFAR-100 and ImageNet datasets in both the anytime prediction setting and the budgeted batch classification setting. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_Resolution_Adaptive_Networks_for_Efficient_Inference_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.07326 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Resolution_Adaptive_Networks_for_Efficient_Inference_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Resolution_Adaptive_Networks_for_Efficient_Inference_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yang_Resolution_Adaptive_Networks_CVPR_2020_supplemental.pdf | null | null |
Normal Assisted Stereo Depth Estimation | Uday Kusupati, Shuo Cheng, Rui Chen, Hao Su | Accurate stereo depth estimation plays a critical role in various 3D tasks in both indoor and outdoor environments. Recently, learning-based multi-view stereo methods have demonstrated competitive performance with limited number of views. However, in challenging scenarios, especially when building cross-view correspondences is hard, these methods still cannot produce satisfying results. In this paper, we study how to enforce the consistency between surface normal and depth at training time to improve the performance. We couple the learning of a multi-view normal estimation module and a multi-view depth estimation module. In addition, we propose a novel consistency loss to train an independent consistency module that refines the depths from depth/normal pairs. We find that the joint learning can improve both the prediction of normal and depth, and the accuracy and smoothness can be further improved by enforcing the consistency. Experiments on MVS, SUN3D, RGBD and Scenes11 demonstrate the effectiveness of our method and state-of-the-art performance. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kusupati_Normal_Assisted_Stereo_Depth_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.10444 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kusupati_Normal_Assisted_Stereo_Depth_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kusupati_Normal_Assisted_Stereo_Depth_Estimation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kusupati_Normal_Assisted_Stereo_CVPR_2020_supplemental.pdf | null | null |
Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF From a Single Image | Zhengqin Li, Mohammad Shafiei, Ravi Ramamoorthi, Kalyan Sunkavalli, Manmohan Chandraker | We propose a deep inverse rendering framework for indoor scenes. From a single RGB image of an arbitrary indoor scene, we obtain a complete scene reconstruction, estimating shape, spatially-varying lighting, and spatially-varying, non-Lambertian surface reflectance. Our novel inverse rendering network incorporates physical insights -- including a spatially-varying spherical Gaussian lighting representation, a differentiable rendering layer to model scene appearance, a cascade structure to iteratively refine the predictions and a bilateral solver for refinement -- allowing us to jointly reason about shape, lighting, and reflectance. Since no existing dataset provides ground truth high quality spatially-varying material and spatially-varying lighting, we propose novel methods to map complex materials to existing indoor scene datasets and a new physically-based GPU renderer to create a large-scale, photorealistic indoor dataset. Experiments show that our framework outperforms previous methods and enables various novel applications like photorealistic object insertion and material editing. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Inverse_Rendering_for_Complex_Indoor_Scenes_Shape_Spatially-Varying_Lighting_and_CVPR_2020_paper.pdf | http://arxiv.org/abs/1905.02722 | https://www.youtube.com/watch?v=RvWlDWtTozw | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Inverse_Rendering_for_Complex_Indoor_Scenes_Shape_Spatially-Varying_Lighting_and_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Inverse_Rendering_for_Complex_Indoor_Scenes_Shape_Spatially-Varying_Lighting_and_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Inverse_Rendering_for_CVPR_2020_supplemental.zip | null | null |
Explorable Super Resolution | Yuval Bahat, Tomer Michaeli | Single image super resolution (SR) has seen major performance leaps in recent years. However, existing methods do not allow exploring the infinitely many plausible reconstructions that might have given rise to the observed low-resolution (LR) image. These different explanations to the LR image may dramatically vary in their textures and fine details, and may often encode completely different semantic information. In this paper, we introduce the task of explorable super resolution. We propose a framework comprising a graphical user interface with a neural network backend, allowing editing the SR output so as to explore the abundance of plausible HR explanations to the LR input. At the heart of our method is a novel module that can wrap any existing SR network, analytically guaranteeing that its SR outputs would precisely match the LR input, when down- sampled. Besides its importance in our setting, this module is guaranteed to decrease the reconstruction error of any SR network it wraps, and can be used to cope with blur kernels that are different from the one the network was trained for. We illustrate our approach in a variety of use cases, ranging from medical imaging and forensics, to graphics. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Bahat_Explorable_Super_Resolution_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.01839 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Bahat_Explorable_Super_Resolution_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Bahat_Explorable_Super_Resolution_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction With Deep T1 Prior | Bo Zhou, S. Kevin Zhou | MRI with multiple protocols is commonly used for diagnosis, but it suffers from a long acquisition time, which yields the image quality vulnerable to say motion artifacts. To accelerate, various methods have been proposed to reconstruct full images from under-sampled k-space data. However, these algorithms are inadequate for two main reasons. Firstly, aliasing artifacts generated in the image domain are structural and non-local, so that sole image domain restoration is insufficient. Secondly, though MRI comprises multiple protocols during one exam, almost all previous studies only employ the reconstruction of an individual protocol using a highly distorted undersampled image as input, leaving the use of fully-sampled short protocol (say T1) as complementary information highly underexplored. In this work, we address the above two limitations by proposing a Dual Domain Recurrent Network (DuDoRNet) with deep T1 prior embedded to simultaneously recover k-space and images for accelerating the acquisition of MRI with a long imaging protocol. Specifically, a Dilated Residual Dense Network (DRDNet) is customized for dual domain restorations from undersampled MRI data. Extensive experiments on different sampling patterns and acceleration rates demonstrate that our method consistently outperforms state-of-the-art methods, and can reconstruct high quality MRI. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhou_DuDoRNet_Learning_a_Dual-Domain_Recurrent_Network_for_Fast_MRI_Reconstruction_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.03799 | https://www.youtube.com/watch?v=0fbHXV-ZmKY | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_DuDoRNet_Learning_a_Dual-Domain_Recurrent_Network_for_Fast_MRI_Reconstruction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_DuDoRNet_Learning_a_Dual-Domain_Recurrent_Network_for_Fast_MRI_Reconstruction_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhou_DuDoRNet_Learning_a_CVPR_2020_supplemental.pdf | null | null |
Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-Weighting | Dongnan Liu, Donghao Zhang, Yang Song, Fan Zhang, Lauren O'Donnell, Heng Huang, Mei Chen, Weidong Cai | Unsupervised domain adaptation (UDA) for nuclei instance segmentation is important for digital pathology, as it alleviates the burden of labor-intensive annotation and domain shift across datasets. In this work, we propose a Cycle Consistency Panoptic Domain Adaptive Mask R-CNN (CyC-PDAM) architecture for unsupervised nuclei segmentation in histopathology images, by learning from fluorescence microscopy images. More specifically, we first propose a nuclei inpainting mechanism to remove the auxiliary generated objects in the synthesized images. Secondly, a semantic branch with a domain discriminator is designed to achieve panoptic-level domain adaptation. Thirdly, in order to avoid the influence of the source-biased features, we propose a task re-weighting mechanism to dynamically add trade-off weights for the task-specific loss functions. Experimental results on three datasets indicate that our proposed method outperforms state-of-the-art UDA methods significantly, and demonstrates a similar performance as fully supervised methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Unsupervised_Instance_Segmentation_in_Microscopy_Images_via_Panoptic_Domain_Adaptation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.02066 | https://www.youtube.com/watch?v=6llwr2PWF7M | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Unsupervised_Instance_Segmentation_in_Microscopy_Images_via_Panoptic_Domain_Adaptation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Unsupervised_Instance_Segmentation_in_Microscopy_Images_via_Panoptic_Domain_Adaptation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Liu_Unsupervised_Instance_Segmentation_CVPR_2020_supplemental.pdf | null | null |
Heterogeneous Knowledge Distillation Using Information Flow Modeling | Nikolaos Passalis, Maria Tzelepi, Anastasios Tefas | Knowledge Distillation (KD) methods are capable of transferring the knowledge encoded in a large and complex teacher into a smaller and faster student. Early methods were usually limited to transferring the knowledge only between the last layers of the networks, while latter approaches were capable of performing multi-layer KD, further increasing the accuracy of the student. However, despite their improved performance, these methods still suffer from several limitations that restrict both their efficiency and flexibility. First, existing KD methods typically ignore that neural networks undergo through different learning phases during the training process, which often requires different types of supervision for each one. Furthermore, existing multi-layer KD methods are usually unable to effectively handle networks with significantly different architectures (heterogeneous KD). In this paper we propose a novel KD method that works by modeling the information flow through the various layers of the teacher model and then train a student model to mimic this information flow. The proposed method is capable of overcoming the aforementioned limitations by using an appropriate supervision scheme during the different phases of the training process, as well as by designing and training an appropriate auxiliary teacher model that acts as a proxy model capable of "explaining" the way the teacher works to the student. The effectiveness of the proposed method is demonstrated using four image datasets and several different evaluation setups. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Passalis_Heterogeneous_Knowledge_Distillation_Using_Information_Flow_Modeling_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.00727 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Passalis_Heterogeneous_Knowledge_Distillation_Using_Information_Flow_Modeling_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Passalis_Heterogeneous_Knowledge_Distillation_Using_Information_Flow_Modeling_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Passalis_Heterogeneous_Knowledge_Distillation_CVPR_2020_supplemental.pdf | null | null |
CARS: Continuous Evolution for Efficient Neural Architecture Search | Zhaohui Yang, Yunhe Wang, Xinghao Chen, Boxin Shi, Chao Xu, Chunjing Xu, Qi Tian, Chang Xu | Searching techniques in most of existing neural architecture search (NAS) algorithms are mainly dominated by differentiable methods for the efficiency reason. In contrast, we develop an efficient continuous evolutionary approach for searching neural networks. Architectures in the population that share parameters within one SuperNet in the latest generation will be tuned over the training dataset with a few epochs. The searching in the next evolution generation will directly inherit both the SuperNet and the population, which accelerates the optimal network generation. The non-dominated sorting strategy is further applied to preserve only results on the Pareto front for accurately updating the SuperNet. Several neural networks with different model sizes and performances will be produced after the continuous search with only 0.4 GPU days. As a result, our framework provides a series of networks with the number of parameters ranging from 3.7M to 5.1M under mobile settings. These networks surpass those produced by the state-of-the-art methods on the benchmark ImageNet dataset. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_CARS_Continuous_Evolution_for_Efficient_Neural_Architecture_Search_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.04977 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_CARS_Continuous_Evolution_for_Efficient_Neural_Architecture_Search_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_CARS_Continuous_Evolution_for_Efficient_Neural_Architecture_Search_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yang_CARS_Continuous_Evolution_CVPR_2020_supplemental.pdf | null | null |
Bi-Directional Interaction Network for Person Search | Wenkai Dong, Zhaoxiang Zhang, Chunfeng Song, Tieniu Tan | Existing works have designed end-to-end frameworks based on Faster-RCNN for person search. Due to the large receptive fields in deep networks, the feature maps of each proposal, cropped from the stem feature maps, involve redundant context information outside the bounding boxes. However, person search is a fine-grained task which needs accurate appearance information. Such context information can make the model fail to focus on persons, so the learned representations lack the capacity to discriminate various identities. To address this issue, we propose a Siamese network which owns an additional instance-aware branch, named Bi-directional Interaction Network (BINet). During the training phase, in addition to scene images, BINet also takes as inputs person patches which help the model discriminate identities based on human appearance. Moreover, two interaction losses are designed to achieve bi-directional interaction between branches at two levels. The interaction can help the model learn more discriminative features for persons in the scene. At the inference stage, only the major branch is applied, so BINet introduces no additional computation. Extensive experiments on two widely used person search benchmarks, CUHK-SYSU and PRW, have shown that our BINet achieves state-of-the-art results among end-to-end methods without loss of efficiency. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Dong_Bi-Directional_Interaction_Network_for_Person_Search_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Dong_Bi-Directional_Interaction_Network_for_Person_Search_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Dong_Bi-Directional_Interaction_Network_for_Person_Search_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
ZSTAD: Zero-Shot Temporal Activity Detection | Lingling Zhang, Xiaojun Chang, Jun Liu, Minnan Luo, Sen Wang, Zongyuan Ge, Alexander Hauptmann | An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos. Currently, the most effective methods of temporal activity detection are based on deep learning, and they typically perform very well with large scale annotated videos for training. However, these methods are limited in real applications due to the unavailable videos about certain activity classes and the time-consuming data annotation. To solve this challenging problem, we propose a novel task setting called zero-shot temporal activity detection (ZSTAD), where activities that have never been seen in training can still be detected. We design an end-to-end deep network based on R-C3D as the architecture for this solution. The proposed network is optimized with an innovative loss function that considers the embeddings of activity labels and their super-classes while learning the common semantics of seen and unseen activities. Experiments on both the THUMOS'14 and the Charades datasets show promising performance in terms of detecting unseen activities. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_ZSTAD_Zero-Shot_Temporal_Activity_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.05583 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_ZSTAD_Zero-Shot_Temporal_Activity_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_ZSTAD_Zero-Shot_Temporal_Activity_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Unpaired Image Super-Resolution Using Pseudo-Supervision | Shunta Maeda | In most studies on learning-based image super-resolution (SR), the paired training dataset is created by downscaling high-resolution (HR) images with a predetermined operation (e.g., bicubic). However, these methods fail to super-resolve real-world low-resolution (LR) images, for which the degradation process is much more complicated and unknown. In this paper, we propose an unpaired SR method using a generative adversarial network that does not require a paired/aligned training dataset. Our network consists of an unpaired kernel/noise correction network and a pseudo-paired SR network. The correction network removes noise and adjusts the kernel of the inputted LR image; then, the corrected clean LR image is upscaled by the SR network. In the training phase, the correction network also produces a pseudo-clean LR image from the inputted HR image, and then a mapping from the pseudo-clean LR image to the inputted HR image is learned by the SR network in a paired manner. Because our SR network is independent of the correction network, well-studied existing network architectures and pixel-wise loss functions can be integrated with the proposed framework. Experiments on diverse datasets show that the proposed method is superior to existing solutions to the unpaired SR problem. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Maeda_Unpaired_Image_Super-Resolution_Using_Pseudo-Supervision_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.11397 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Maeda_Unpaired_Image_Super-Resolution_Using_Pseudo-Supervision_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Maeda_Unpaired_Image_Super-Resolution_Using_Pseudo-Supervision_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Maeda_Unpaired_Image_Super-Resolution_CVPR_2020_supplemental.pdf | null | null |
Training Quantized Neural Networks With a Full-Precision Auxiliary Module | Bohan Zhuang, Lingqiao Liu, Mingkui Tan, Chunhua Shen, Ian Reid | In this paper, we seek to tackle a challenge in training low-precision networks: the notorious difficulty in propagating gradient through a low-precision network due to the non-differentiable quantization function. We propose a solution by training the low-precision network with a full-precision auxiliary module. Specifically, during training, we construct a mix-precision network by augmenting the original low-precision network with the full precision auxiliary module. Then the augmented mix-precision network and the low-precision network are jointly optimized. This strategy creates additional full-precision routes to update the parameters of the low-precision model, thus making the gradient back-propagates more easily. At the inference time, we discard the auxiliary module without introducing any computational complexity to the low-precision network. We evaluate the proposed method on image classification and object detection over various quantization approaches and show consistent performance increase. In particular, we achieve near lossless performance to the full-precision model by using a 4-bit detector, which is of great practical value. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhuang_Training_Quantized_Neural_Networks_With_a_Full-Precision_Auxiliary_Module_CVPR_2020_paper.pdf | http://arxiv.org/abs/1903.11236 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhuang_Training_Quantized_Neural_Networks_With_a_Full-Precision_Auxiliary_Module_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhuang_Training_Quantized_Neural_Networks_With_a_Full-Precision_Auxiliary_Module_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
ReSprop: Reuse Sparsified Backpropagation | Negar Goli, Tor M. Aamodt | The success of Convolutional Neural Networks (CNNs) in various applications is accompanied by a significant increase in computation and training time. In this work, we focus on accelerating training by observing that about 90% of gradients are reusable during training. Leveraging this observation, we propose a new algorithm, Reuse-Sparse-Backprop (ReSprop), as a method to sparsify gradient vectors during CNN training. ReSprop maintains state-of-the-art accuracy on CIFAR-10, CIFAR-100, and ImageNet datasets with less than 1.1% accuracy loss while enabling a reduction in back-propagation computations by a factor of 10x resulting in a 2.7x overall speedup in training. As the computation reduction introduced by Re-Sprop is accomplished by introducing fine-grained sparsity that reduces computation efficiency on GPUs, we introduce a generic sparse convolution neural network accelerator (GSCN), which is designed to accelerate sparse back-propagation convolutions. When combined with ReSprop, GSCN achieves 8.0x and 7.2x speedup in the backward pass on ResNet34 and VGG16 versus a GTX 1080 Ti GPU. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Goli_ReSprop_Reuse_Sparsified_Backpropagation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Goli_ReSprop_Reuse_Sparsified_Backpropagation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Goli_ReSprop_Reuse_Sparsified_Backpropagation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network | Shaolin Su, Qingsen Yan, Yu Zhu, Cheng Zhang, Xin Ge, Jinqiu Sun, Yanning Zhang | Blind image quality assessment (BIQA) for authentically distorted images has always been a challenging problem, since images captured in the wild include varies contents and diverse types of distortions. The vast majority of prior BIQA methods focus on how to predict synthetic image quality, but fail when applied to real-world distorted images. To deal with the challenge, we propose a self-adaptive hyper network architecture to blind assess image quality in the wild. We separate the IQA procedure into three stages including content understanding, perception rule learning and quality predicting. After extracting image semantics, perception rule is established adaptively by a hyper network, and then adopted by a quality prediction network. In our model, image quality can be estimated in a self-adaptive manner, thus generalizes well on diverse images captured in the wild. Experimental results verify that our approach not only outperforms the state-of-the-art methods on challenging authentic image databases but also achieves competing performances on synthetic image databases, though it is not explicitly designed for the synthetic task. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Su_Blindly_Assess_Image_Quality_in_the_Wild_Guided_by_a_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=qksOm2bHRyg | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Su_Blindly_Assess_Image_Quality_in_the_Wild_Guided_by_a_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Su_Blindly_Assess_Image_Quality_in_the_Wild_Guided_by_a_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Su_Blindly_Assess_Image_CVPR_2020_supplemental.pdf | null | null |
Bundle Adjustment on a Graph Processor | Joseph Ortiz, Mark Pupilli, Stefan Leutenegger, Andrew J. Davison | Graph processors such as Graphcore's Intelligence Processing Unit (IPU) are part of the major new wave of novel computer architecture for AI, and have a general design with massively parallel computation, distributed on-chip memory and very high inter-core communication bandwidth which allows breakthrough performance for message passing algorithms on arbitrary graphs. We show for the first time that the classical computer vision problem of bundle adjustment (BA) can be solved extremely fast on a graph processor using Gaussian Belief Propagation. Our simple but fully parallel implementation uses the 1216 cores on a single IPU chip to, for instance, solve a real BA problem with 125 keyframes and 1919 points in under 40ms, compared to 1450ms for the Ceres CPU library. Further code optimisation will surely increase this difference on static problems, but we argue that the real promise of graph processing is for flexible in-place optimisation of general, dynamically changing factor graphs representing Spatial AI problems. We give indications of this with experiments showing the ability of GBP to efficiently solve incremental SLAM problems, and deal with robust cost functions and different types of factors. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ortiz_Bundle_Adjustment_on_a_Graph_Processor_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.03134 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ortiz_Bundle_Adjustment_on_a_Graph_Processor_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ortiz_Bundle_Adjustment_on_a_Graph_Processor_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Multi-View Neural Human Rendering | Minye Wu, Yuehao Wang, Qiang Hu, Jingyi Yu | We present an end-to-end Neural Human Renderer (NHR) for dynamic human captures under the multi-view setting. NHR adopts PointNet++ for feature extraction (FE) to enable robust 3D correspondence matching on low quality, dynamic 3D reconstructions. To render new views, we map 3D features onto the target camera as a 2D feature map and employ an anti-aliased CNN to handle holes and noises. Newly synthesized views from NHR can be further used to construct visual hulls to handle textureless and/or dark regions such as black clothing. Comprehensive experiments show NHR significantly outperforms the state-of-the-art neural and image-based rendering techniques, especially on hands, hair, nose, foot, etc. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wu_Multi-View_Neural_Human_Rendering_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_Multi-View_Neural_Human_Rendering_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_Multi-View_Neural_Human_Rendering_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Self-Supervised Monocular Trained Depth Estimation Using Self-Attention and Discrete Disparity Volume | Adrian Johnston, Gustavo Carneiro | Monocular depth estimation has become one of the most studied applications in computer vision, where the most accurate approaches are based on fully supervised learning models. However, the acquisition of accurate and large ground truth data sets to model these fully supervised methods is a major challenge for the further development of the area. Self-supervised methods trained with monocular videos constitute one the most promising approaches to mitigate the challenge mentioned above due to the wide-spread availability of training data. Consequently, they have been intensively studied, where the main ideas explored consist of different types of model architectures, loss functions, and occlusion masks to address non-rigid motion. In this paper, we propose two new ideas to improve self-supervised monocular trained depth estimation: 1) self-attention, and 2) discrete disparity prediction. Compared with the usual localised convolution operation, self-attention can explore a more general contextual information that allows the inference of similar disparity values at non-contiguous regions of the image. Discrete disparity prediction has been shown by fully supervised methods to provide a more robust and sharper depth estimation than the more common continuous disparity prediction, besides enabling the estimation of depth uncertainty. We show that the extension of the state-of-the-art self-supervised monocular trained depth estimator Monodepth2 with these two ideas allows us to design a model that produces the best results in the field in KITTI 2015 and Make3D, closing the gap with respect self-supervised stereo training and fully supervised approaches. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Johnston_Self-Supervised_Monocular_Trained_Depth_Estimation_Using_Self-Attention_and_Discrete_Disparity_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13951 | https://www.youtube.com/watch?v=_CLIht3A5pw | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Johnston_Self-Supervised_Monocular_Trained_Depth_Estimation_Using_Self-Attention_and_Discrete_Disparity_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Johnston_Self-Supervised_Monocular_Trained_Depth_Estimation_Using_Self-Attention_and_Discrete_Disparity_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Johnston_Self-Supervised_Monocular_Trained_CVPR_2020_supplemental.pdf | null | null |
Joint Filtering of Intensity Images and Neuromorphic Events for High-Resolution Noise-Robust Imaging | Zihao W. Wang, Peiqi Duan, Oliver Cossairt, Aggelos Katsaggelos, Tiejun Huang, Boxin Shi | We present a novel computational imaging system with high resolution and low noise. Our system consists of a traditional video camera which captures high-resolution intensity images, and an event camera which encodes high-speed motion as a stream of asynchronous binary events. To process the hybrid input, we propose a unifying framework that first bridges the two sensing modalities via a noise-robust motion compensation model, and then performs joint image filtering. The filtered output represents the temporal gradient of the captured space-time volume, which can be viewed as motion-compensated event frames with high resolution and low noise. Therefore, the output can be widely applied to many existing event-based algorithms that are highly dependent on spatial resolution and noise robustness. In experimental results performed on both publicly available datasets as well as our contributing RGB-DAVIS dataset, we show systematic performance improvement in applications such as high frame-rate video synthesis, feature/corner detection and tracking, as well as high dynamic range image reconstruction. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Joint_Filtering_of_Intensity_Images_and_Neuromorphic_Events_for_High-Resolution_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=hEkIgJ1eJ5g | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Joint_Filtering_of_Intensity_Images_and_Neuromorphic_Events_for_High-Resolution_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Joint_Filtering_of_Intensity_Images_and_Neuromorphic_Events_for_High-Resolution_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_Joint_Filtering_of_CVPR_2020_supplemental.pdf | null | null |
Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-Based Approach | Haichuan Yang, Shupeng Gui, Yuhao Zhu, Ji Liu | Deep Neural Networks (DNNs) are applied in a wide range of usecases. There is an increased demand for deploying DNNs on devices that do not have abundant resources such as memory and computation units. Recently, network compression through a variety of techniques such as pruning and quantization have been proposed to reduce the resource requirement. A key parameter that all existing compression techniques are sensitive to is the compression ratio (e.g., pruning sparsity, quantization bitwidth) of each layer. Traditional solutions treat the compression ratios of each layer as hyper-parameters, and tune them using human heuristic. Recent researchers start using black-box hyper-parameter optimizations, but they will introduce new hyper-parameters and have efficiency issue. In this paper, we propose a framework to jointly prune and quantize the DNNs automatically according to a target model size without using any hyper-parameters to manually set the compression ratio for each layer. In the experiments, we show that our framework can compress the weights data of ResNet-50 to be 836x smaller without accuracy loss on CIFAR-10, and compress AlexNet to be 205x smaller without accuracy loss on ImageNet classification. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_Automatic_Neural_Network_Compression_by_Sparsity-Quantization_Joint_Learning_A_Constrained_CVPR_2020_paper.pdf | http://arxiv.org/abs/1910.05897 | https://www.youtube.com/watch?v=maeD0qvzBec | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Automatic_Neural_Network_Compression_by_Sparsity-Quantization_Joint_Learning_A_Constrained_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Automatic_Neural_Network_Compression_by_Sparsity-Quantization_Joint_Learning_A_Constrained_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
When2com: Multi-Agent Perception via Communication Graph Grouping | Yen-Cheng Liu, Junjiao Tian, Nathaniel Glaser, Zsolt Kira | While significant advances have been made for single-agent perception, many applications require multiple sensing agents and cross-agent communication due to benefits such as coverage and robustness. It is therefore critical to develop frameworks which support multi-agent collaborative perception in a distributed and bandwidth-efficient manner. In this paper, we address the collaborative perception problem, where one agent is required to perform a perception task and can communicate and share information with other agents on the same task. Specifically, we propose a communication framework by learning both to construct communication groups and decide when to communicate. We demonstrate the generalizability of our framework on two different perception tasks and show that it significantly reduces communication bandwidth while maintaining superior performance. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_When2com_Multi-Agent_Perception_via_Communication_Graph_Grouping_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.00176 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_When2com_Multi-Agent_Perception_via_Communication_Graph_Grouping_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_When2com_Multi-Agent_Perception_via_Communication_Graph_Grouping_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
MAGSAC++, a Fast, Reliable and Accurate Robust Estimator | Daniel Barath, Jana Noskova, Maksym Ivashechkin, Jiri Matas | We propose MAGSAC++ and Progressive NAPSAC sampler, P-NAPSAC in short. In MAGSAC++, we replace the model quality and polishing functions of the original method by an iteratively re-weighted least-squares fitting with weights determined via marginalizing over the noise scale. MAGSAC++ is fast -- often an order of magnitude faster -- and more geometrically accurate than MAGSAC. P-NAPSAC merges the advantages of local and global sampling by drawing samples from gradually growing neighborhoods. Exploiting that nearby points are more likely to originate from the same geometric model, P-NAPSAC finds local structures earlier than global samplers. We show that the progressive spatial sampling in P-NAPSAC can be integrated with PROSAC sampling, which is applied to the first, location-defining, point. The methods are tested on homography and fundamental matrix fitting on six publicly available datasets. MAGSAC combined with P-NAPSAC sampler is superior to state-of-the-art robust estimators in terms of speed, accuracy and failure rate. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Barath_MAGSAC_a_Fast_Reliable_and_Accurate_Robust_Estimator_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Barath_MAGSAC_a_Fast_Reliable_and_Accurate_Robust_Estimator_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Barath_MAGSAC_a_Fast_Reliable_and_Accurate_Robust_Estimator_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Barath_MAGSAC_a_Fast_CVPR_2020_supplemental.zip | null | null |
Organ at Risk Segmentation for Head and Neck Cancer Using Stratified Learning and Neural Architecture Search | Dazhou Guo, Dakai Jin, Zhuotun Zhu, Tsung-Ying Ho, Adam P. Harrison, Chun-Hung Chao, Jing Xiao, Le Lu | OAR segmentation is a critical step in radiotherapy of head and neck (H&N) cancer, where inconsistencies across radiation oncologists and prohibitive labor costs motivate automated approaches. However, leading methods using standard fully convolutional network workflows that are challenged when the number of OARs becomes large, e.g. > 40. For such scenarios, insights can be gained from the stratification approaches seen in manual clinical OAR delineation. This is the goal of our work, where we introduce stratified organ at risk segmentation (SOARS), an approach that stratifies OARs into anchor, mid-level, and small & hard (S&H) categories. SOARS stratifies across two dimensions. The first dimension is that distinct processing pipelines are used for each OAR category. In particular, inspired by clinical practices, anchor OARs are used to guide the mid-level and S&H categories. The second dimension is that distinct network architectures are used to manage the significant contrast, size, and anatomy variations between different OARs. We use differentiable neural architecture search (NAS), allowing the network to choose among 2D, 3D or Pseudo-3D convolutions. Extensive 4-fold cross-validation on 142 H&N cancer patients with 42 manually labeled OARs, the most comprehensive OAR dataset to date, demonstrates that both pipeline- and NAS-stratification significantly improves quantitative performance over the state-of-the-art (from 69.52% to 73.68% in absolute Dice scores). Thus, SOARS provides a powerful and principled means to manage the highly complex segmentation space of OARs. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Guo_Organ_at_Risk_Segmentation_for_Head_and_Neck_Cancer_Using_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.08426 | https://www.youtube.com/watch?v=MnZ366oy10Q | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Organ_at_Risk_Segmentation_for_Head_and_Neck_Cancer_Using_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Organ_at_Risk_Segmentation_for_Head_and_Neck_Cancer_Using_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Guo_Organ_at_Risk_CVPR_2020_supplemental.pdf | null | null |
Learning Human-Object Interaction Detection Using Interaction Points | Tiancai Wang, Tong Yang, Martin Danelljan, Fahad Shahbaz Khan, Xiangyu Zhang, Jian Sun | Understanding interactions between humans and objects is one of the fundamental problems in visual classification and an essential step towards detailed scene understanding. Human-object interaction (HOI) detection strives to localize both the human and an object as well as the identification of complex interactions between them. Most existing HOI detection approaches are instance-centric where interactions between all possible human-object pairs are predicted based on appearance features and coarse spatial information. We argue that appearance features alone are insufficient to capture complex human-object interactions. In this paper, we therefore propose a novel fully-convolutional approach that directly detects the interactions between human-object pairs. Our network predicts interaction points, which directly localize and classify the inter-action. Paired with the densely predicted interaction vectors, the interactions are associated with human and object detections to obtain final predictions. To the best of our knowledge, we are the first to propose an approach where HOI detection is posed as a keypoint detection and grouping problem. Experiments are performed on two popular benchmarks: V-COCO and HICO-DET. Our approach sets a new state-of-the-art on both datasets. Code is available at https://github.com/vaesl/IP-Net. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Learning_Human-Object_Interaction_Detection_Using_Interaction_Points_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.14023 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Learning_Human-Object_Interaction_Detection_Using_Interaction_Points_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Learning_Human-Object_Interaction_Detection_Using_Interaction_Points_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Deep Kinematics Analysis for Monocular 3D Human Pose Estimation | Jingwei Xu, Zhenbo Yu, Bingbing Ni, Jiancheng Yang, Xiaokang Yang, Wenjun Zhang | For monocular 3D pose estimation conditioned on 2D detection, noisy/unreliable input is a key obstacle in this task. Simple structure constraints attempting to tackle this problem, e.g., symmetry loss and joint angle limit, could only provide marginal improvements and are commonly treated as auxiliary losses in previous researches. Thus it still remains challenging about how to effectively utilize the power of human prior knowledge for this task. In this paper, we propose to address above issue in a systematic view. Firstly, we show that optimizing the kinematics structure of noisy 2D inputs is critical to obtain accurate 3D estimations. Secondly, based on corrected 2D joints, we further explicitly decompose articulated motion with human topology, which leads to more compact 3D static structure easier for estimation. Finally, temporal refinement emphasizing the validity of 3D dynamic structure is naturally developed to pursue more accurate result. Above three steps are seamlessly integrated into deep neural models, which form a deep kinematics analysis pipeline concurrently considering the static/dynamic structure of 2D inputs and 3D outputs. Extensive experiments show that proposed framework achieves state-of-the-art performance on two widely used 3D human action datasets. Meanwhile, targeted ablation study shows that each former step is critical for the latter one to obtain promising results. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xu_Deep_Kinematics_Analysis_for_Monocular_3D_Human_Pose_Estimation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Deep_Kinematics_Analysis_for_Monocular_3D_Human_Pose_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Deep_Kinematics_Analysis_for_Monocular_3D_Human_Pose_Estimation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation | Yunhan Zhao, Shu Kong, Daeyun Shin, Charless Fowlkes | Leveraging synthetically rendered data offers great potential to improve monocular depth estimation and other geometric estimation tasks, but closing the synthetic-real domain gap is a non-trivial and important task. While much recent work has focused on unsupervised domain adaptation, we consider a more realistic scenario where a large amount of synthetic training data is supplemented by a small set of real images with ground-truth. In this setting, we find that existing domain translation approaches are difficult to train and offer little advantage over simple baselines that use a mix of real and synthetic data. A key failure mode is that real-world images contain novel objects and clutter not present in synthetic training. This high-level domain shift isn't handled by existing image translation models. Based on these observations, we develop an attention module that learns to identify and remove difficult out-of-domain regions in real images in order to improve depth prediction for a model trained primarily on synthetic data. We carry out extensive experiments to validate our attend-remove-complete approach (ARC) and find that it significantly outperforms state-of-the-art domain adaptation methods for depth prediction. Visualizing the removed regions provides interpretable insights into the synthetic-real domain gap. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhao_Domain_Decluttering_Simplifying_Images_to_Mitigate_Synthetic-Real_Domain_Shift_and_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.12114 | https://www.youtube.com/watch?v=bQMxtAVYFrg | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Domain_Decluttering_Simplifying_Images_to_Mitigate_Synthetic-Real_Domain_Shift_and_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Domain_Decluttering_Simplifying_Images_to_Mitigate_Synthetic-Real_Domain_Shift_and_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhao_Domain_Decluttering_Simplifying_CVPR_2020_supplemental.pdf | null | null |
End-to-End Illuminant Estimation Based on Deep Metric Learning | Bolei Xu, Jingxin Liu, Xianxu Hou, Bozhi Liu, Guoping Qiu | Previous deep learning approaches to color constancy usually directly estimate illuminant value from input image. Such approaches might suffer heavily from being sensitive to the variation of image content. To overcome this problem, we introduce a deep metric learning approach named Illuminant-Guided Triplet Network (IGTN) to color constancy. IGTN generates an Illuminant Consistent and Discriminative Feature (ICDF) for achieving robust and accurate illuminant color estimation. ICDF is composed of semantic and color features based on a learnable color histogram scheme. In the ICDF space, regardless of the similarities of their contents, images taken under the same or similar illuminants are placed close to each other and at the same time images taken under different illuminants are placed far apart. We also adopt an end-to-end training strategy to simultaneously group image features and estimate illuminant value, and thus our approach does not have to classify illuminant in a separate module. We evaluate our method on two public datasets and demonstrate our method outperforms state-of-the-art approaches. Furthermore, we demonstrate that our method is less sensitive to image appearances, and can achieve more robust and consistent results than other methods on a High Dynamic Range dataset. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xu_End-to-End_Illuminant_Estimation_Based_on_Deep_Metric_Learning_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_End-to-End_Illuminant_Estimation_Based_on_Deep_Metric_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_End-to-End_Illuminant_Estimation_Based_on_Deep_Metric_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
PatchVAE: Learning Local Latent Codes for Recognition | Kamal Gupta, Saurabh Singh, Abhinav Shrivastava | Unsupervised representation learning holds the promise of exploiting large amounts of unlabeled data to learn general representations. A promising technique for unsupervised learning is the framework of Variational Auto-encoders (VAEs). However, unsupervised representations learned by VAEs are significantly outperformed by those learned by supervised learning for recognition. Our hypothesis is that to learn useful representations for recognition the model needs to be encouraged to learn about repeating and consistent patterns in data. Drawing inspiration from the mid-level representation discovery work, we propose PatchVAE, that reasons about images at patch level. Our key contribution is a bottleneck formulation that encourages mid-level style representations in the VAE framework. Our experiments demonstrate that representations learned by our method perform much better on the recognition tasks compared to those learned by vanilla VAEs. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gupta_PatchVAE_Learning_Local_Latent_Codes_for_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.03623 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gupta_PatchVAE_Learning_Local_Latent_Codes_for_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gupta_PatchVAE_Learning_Local_Latent_Codes_for_Recognition_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Gupta_PatchVAE_Learning_Local_CVPR_2020_supplemental.pdf | null | null |
FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation | Xiang Li, Tianhan Wei, Yau Pun Chen, Yu-Wing Tai, Chi-Keung Tang | Over the past few years, we have witnessed the success of deep learning in image recognition thanks to the availability of large-scale human-annotated datasets such as PASCAL VOC, ImageNet, and COCO. Although these datasets have covered a wide range of object categories, there are still a significant number of objects that are not included. Can we perform the same task without a lot of human annotations? In this paper, we are interested in few-shot object segmentation where the number of annotated training examples are limited to 5 only. To evaluate and validate the performance of our approach, we have built a few-shot segmentation dataset, FSS-1000, which consists of 1000 object classes with pixelwise annotation of ground-truth segmentation. Unique in FSS-1000, our dataset contains significant number of objects that have never been seen or annotated in previous datasets, such as tiny daily objects, merchandise, cartoon characters, logos, etc. We build our baseline model using standard backbone networks such as VGG-16, ResNet-101, and Inception. To our surprise, we found that training our model from scratch using FSS-1000 achieves comparable and even better results than training with weights pre-trained by ImageNet which is more than 100 times larger than FSS-1000. Both our approach and dataset are simple, effective, and easily extensible to learn segmentation of new object classes given very few annotated training examples. Dataset is available at https://github.com/HKUSTCV/FSS-1000 | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_FSS-1000_A_1000-Class_Dataset_for_Few-Shot_Segmentation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_FSS-1000_A_1000-Class_Dataset_for_Few-Shot_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_FSS-1000_A_1000-Class_Dataset_for_Few-Shot_Segmentation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_FSS-1000_A_1000-Class_CVPR_2020_supplemental.zip | https://cove.thecvf.com/datasets/338 | null |
Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-Resolvers | Shady Abu Hussein, Tom Tirer, Raja Giryes | The single image super-resolution task is one of the most examined inverse problems in the past decade. In the recent years, Deep Neural Networks (DNNs) have shown superior performance over alternative methods when the acquisition process uses a fixed known downscaling kernel---typically a bicubic kernel. However, several recent works have shown that in practical scenarios, where the test data mismatch the training data (e.g. when the downscaling kernel is not the bicubic kernel or is not available at training), the leading DNN methods suffer from a huge performance drop. Inspired by the literature on generalized sampling, in this work we propose a method for improving the performance of DNNs that have been trained with a fixed kernel on observations acquired by other kernels. For a known kernel, we design a closed-form correction filter that modifies the low-resolution image to match one which is obtained by another kernel (e.g. bicubic), and thus improves the results of existing pre-trained DNNs. For an unknown kernel, we extend this idea and propose an algorithm for blind estimation of the required correction filter. We show that our approach outperforms other super-resolution methods, which are designed for general downscaling kernels. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Abu_Hussein_Correction_Filter_for_Single_Image_Super-Resolution_Robustifying_Off-the-Shelf_Deep_Super-Resolvers_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=r0CjykCZnlk | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Abu_Hussein_Correction_Filter_for_Single_Image_Super-Resolution_Robustifying_Off-the-Shelf_Deep_Super-Resolvers_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Abu_Hussein_Correction_Filter_for_Single_Image_Super-Resolution_Robustifying_Off-the-Shelf_Deep_Super-Resolvers_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Abu_Hussein_Correction_Filter_for_CVPR_2020_supplemental.pdf | null | null |
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning | Tianlong Chen, Sijia Liu, Shiyu Chang, Yu Cheng, Lisa Amini, Zhangyang Wang | Pretrained models from self-supervision are prevalently used in fine-tuning downstream tasks faster or for better accuracy. However, gaining robustness from pretraining is left unexplored. We introduce adversarial training into self-supervision, to provide general-purpose robust pretrained models for the first time. We find these robust pretrained models can benefit the subsequent fine-tuning in two ways: i) boosting final model robustness; ii) saving the computation cost, if proceeding towards adversarial fine-tuning. We conduct extensive experiments to demonstrate that the proposed framework achieves large performance margins (eg, 3.83% on robust accuracy and 1.3% on standard accuracy, on the CIFAR-10 dataset), compared with the conventional end-to-end adversarial training baseline. Moreover, we find that different self-supervised pretrained models have diverse adversarial vulnerability. It inspires us to ensemble several pretraining tasks, which boosts robustness more. Our ensemble strategy contributes to a further improvement of 3.59% on robust accuracy, while maintaining a slightly higher standard accuracy on CIFAR-10. Our codes are available at https://github.com/TAMU-VITA/Adv-SS-Pretraining. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Adversarial_Robustness_From_Self-Supervised_Pre-Training_to_Fine-Tuning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12862 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Adversarial_Robustness_From_Self-Supervised_Pre-Training_to_Fine-Tuning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Adversarial_Robustness_From_Self-Supervised_Pre-Training_to_Fine-Tuning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_Adversarial_Robustness_From_CVPR_2020_supplemental.pdf | null | null |
Efficient Adversarial Training With Transferable Adversarial Examples | Haizhong Zheng, Ziqi Zhang, Juncheng Gu, Honglak Lee, Atul Prakash | Adversarial training is an effective defense method to protect classification models against adversarial attacks. However, one limitation of this approach is that it can require orders of magnitude additional training time due to high cost of generating strong adversarial examples during training. In this paper, we first show that there is high transferability between models from neighboring epochs in the same training process, i.e., adversarial examples from one epoch continue to be adversarial in subsequent epochs. Leveraging this property, we propose a novel method, Adversarial Training with Transferable Adversarial Examples (ATTA), that can enhance the robustness of trained models and greatly improve the training efficiency by accumulating adversarial perturbations through epochs. Compared to state-of-the-art adversarial training methods, ATTA enhances adversarial accuracy by up to 7.2% on CIFAR10 and requires 12 14x less training time on MNIST and CIFAR10 datasets with comparable model robustness. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zheng_Efficient_Adversarial_Training_With_Transferable_Adversarial_Examples_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.11969 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Efficient_Adversarial_Training_With_Transferable_Adversarial_Examples_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Efficient_Adversarial_Training_With_Transferable_Adversarial_Examples_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zheng_Efficient_Adversarial_Training_CVPR_2020_supplemental.pdf | null | null |
Adversarial Texture Optimization From RGB-D Scans | Jingwei Huang, Justus Thies, Angela Dai, Abhijit Kundu, Chiyu "Max" Jiang, Leonidas J. Guibas, Matthias Niessner, Thomas Funkhouser | Realistic color texture generation is an important step in RGB-D surface reconstruction, but remains challenging in practice due to inaccuracies in reconstructed geometry, misaligned camera poses, and view-dependent imaging artifacts. In this work, we present a novel approach for color texture generation using a conditional adversarial loss obtained from weakly-supervised views. Specifically, we propose an approach to produce photorealistic textures for approximate surfaces, even from misaligned images, by learning an objective function that is robust to these errors. The key idea of our approach is to learn a patch-based conditional discriminator which guides the texture optimization to be tolerant to misalignments. Our discriminator takes a synthesized view and a real image, and evaluates whether the synthesized one is realistic, under a broadened definition of realism. We train the discriminator by providing as `real' examples pairs of input views and their misaligned versions -- so that the learned adversarial loss will tolerate errors from the scans. Experiments on synthetic and real data under quantitative or qualitative evaluation demonstrate the advantage of our approach in comparison to state of the art. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Huang_Adversarial_Texture_Optimization_From_RGB-D_Scans_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Adversarial_Texture_Optimization_From_RGB-D_Scans_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Adversarial_Texture_Optimization_From_RGB-D_Scans_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Huang_Adversarial_Texture_Optimization_CVPR_2020_supplemental.zip | null | null |
PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization | Shunsuke Saito, Tomas Simon, Jason Saragih, Hanbyul Joo | Recent advances in image-based 3D human shape estimation have been driven by the significant improvement in representation power afforded by deep neural networks. Although current approaches have demonstrated the potential in real world settings, they still fail to produce reconstructions with the level of detail often present in the input images. We argue that this limitation stems primarily form two conflicting requirements; accurate predictions require large context, but precise predictions require high resolution. Due to memory limitations in current hardware, previous approaches tend to take low resolution images as input to cover large spatial context, and produce less precise (or low resolution) 3D estimates as a result. We address this limitation by formulating a multi-level architecture that is end-to-end trainable. A coarse level observes the whole image at lower resolution and focuses on holistic reasoning. This provides context to an fine level which estimates highly detailed geometry by observing higher-resolution images. We demonstrate that our approach significantly outperforms existing state-of-the-art techniques on single image human shape reconstruction by fully leveraging 1k-resolution input images. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Saito_PIFuHD_Multi-Level_Pixel-Aligned_Implicit_Function_for_High-Resolution_3D_Human_Digitization_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00452 | https://www.youtube.com/watch?v=ufwjC_MtF9M | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Saito_PIFuHD_Multi-Level_Pixel-Aligned_Implicit_Function_for_High-Resolution_3D_Human_Digitization_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Saito_PIFuHD_Multi-Level_Pixel-Aligned_Implicit_Function_for_High-Resolution_3D_Human_Digitization_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
TextureFusion: High-Quality Texture Acquisition for Real-Time RGB-D Scanning | Joo Ho Lee, Hyunho Ha, Yue Dong, Xin Tong, Min H. Kim | Real-time RGB-D scanning technique has become widely used to progressively scan objects with a hand-held sensor. Existing online methods restore color information per voxel, and thus their quality is often limited by the tradeoff between spatial resolution and time performance. Also, such methods often suffer from blurred artifacts in the captured texture. Traditional offline texture mapping methods with non-rigid warping assume that the reconstructed geometry and all input views are obtained in advance, and the optimization takes a long time to compute mesh parameterization and warp parameters, which prevents them from being used in real-time applications. In this work, we propose a progressive texture-fusion method specially designed for real-time RGB-D scanning. To this end, we first devise a novel texture-tile voxel grid, where texture tiles are embedded in the voxel grid of the signed distance function, allowing for high-resolution texture mapping on the low-resolution geometry volume. Instead of using expensive mesh parameterization, we associate vertices of implicit geometry directly with texture coordinates. Second, we introduce real-time texture warping that applies a spatially-varying perspective mapping to input images so that texture warping efficiently mitigates the mismatch between the intermediate geometry and the current input view. It allows us to enhance the quality of texture over time while updating the geometry in real-time. The results demonstrate that the quality of our real-time texture mapping is highly competitive to that of exhaustive offline texture warping methods. Our method is also capable of being integrated into existing RGB-D scanning frameworks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lee_TextureFusion_High-Quality_Texture_Acquisition_for_Real-Time_RGB-D_Scanning_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=VnqaqkSgAQg | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_TextureFusion_High-Quality_Texture_Acquisition_for_Real-Time_RGB-D_Scanning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_TextureFusion_High-Quality_Texture_Acquisition_for_Real-Time_RGB-D_Scanning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lee_TextureFusion_High-Quality_Texture_CVPR_2020_supplemental.zip | null | null |
TomoFluid: Reconstructing Dynamic Fluid From Sparse View Videos | Guangming Zang, Ramzi Idoughi, Congli Wang, Anthony Bennett, Jianguo Du, Scott Skeen, William L. Roberts, Peter Wonka, Wolfgang Heidrich | Visible light tomography is a promising and increasingly popular technique for fluid imaging. However, the use of a sparse number of viewpoints in the capturing setups makes the reconstruction of fluid flows very challenging. In this paper, we present a state-of-the-art 4D tomographic reconstruction framework that integrates several regularizers into a multi-scale matrix free optimization algorithm. In addition to existing regularizers, we propose two new regularizers for improved results: a regularizer based on view interpolation of projected images and a regularizer to encourage reprojection consistency. We demonstrate our method with extensive experiments on both simulated and real data. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zang_TomoFluid_Reconstructing_Dynamic_Fluid_From_Sparse_View_Videos_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zang_TomoFluid_Reconstructing_Dynamic_Fluid_From_Sparse_View_Videos_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zang_TomoFluid_Reconstructing_Dynamic_Fluid_From_Sparse_View_Videos_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Point Cloud Completion by Skip-Attention Network With Hierarchical Folding | Xin Wen, Tianyang Li, Zhizhong Han, Yu-Shen Liu | Point cloud completion aims to infer the complete geometries for missing regions of 3D objects from incomplete ones. Previous methods usually predict the complete point cloud based on the global shape representation extracted from the incomplete input. However, the global representation often suffers from the information loss of structure details on local regions of incomplete point cloud. To address this problem, we propose Skip-Attention Network (SA-Net) for 3D point cloud completion. Our main contributions lie in the following two-folds. First, we propose a skip-attention mechanism to effectively exploit the local structure details of incomplete point clouds during the inference of missing parts. The skip-attention mechanism selectively conveys geometric information from the local regions of incomplete point clouds for the generation of complete ones at different resolutions, where the skip-attention reveals the completion process in an interpretable way. Second, in order to fully utilize the selected geometric information encoded by skip-attention mechanism at different resolutions, we propose a novel structure-preserving decoder with hierarchical folding for complete shape generation. The hierarchical folding preserves the structure of complete point cloud generated in upper layer by progressively detailing the local regions, using the skip-attentioned geometry at the same resolution. We conduct comprehensive experiments on ShapeNet and KITTI datasets, which demonstrate that the proposed SA-Net outperforms the state-of-the-art point cloud completion methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wen_Point_Cloud_Completion_by_Skip-Attention_Network_With_Hierarchical_Folding_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.03871 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wen_Point_Cloud_Completion_by_Skip-Attention_Network_With_Hierarchical_Folding_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wen_Point_Cloud_Completion_by_Skip-Attention_Network_With_Hierarchical_Folding_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wen_Point_Cloud_Completion_CVPR_2020_supplemental.pdf | null | null |
Revisiting Knowledge Distillation via Label Smoothing Regularization | Li Yuan, Francis EH Tay, Guilin Li, Tao Wang, Jiashi Feng | Knowledge Distillation (KD) aims to distill the knowledge of a cumbersome teacher model into a lightweight student model. Its success is generally attributed to the privileged information on similarities among categories provided by the teacher model, and in this sense, only strong teacher models are deployed to teach weaker students in practice. In this work, we challenge this common belief by following experimental observations: 1) beyond the acknowledgment that the teacher can improve the student, the student can also enhance the teacher significantly by reversing the KD procedure; 2) a poorly-trained teacher with much lower accuracy than the student can still improve the latter significantly. To explain these observations, we provide a theoretical analysis of the relationships between KD and label smoothing regularization. We prove that 1) KD is a type of learned label smoothing regularization and 2) label smoothing regularization provides a virtual teacher model for KD. From these results, we argue that the success of KD is not fully due to the similarity information between categories from teachers, but also to the regularization of soft targets, which is equally or even more important. Based on these analyses, we further propose a novel Teacher-free Knowledge Distillation (Tf-KD) framework, where a student model learns from itself or manually-designed regularization distribution. The Tf-KD achieves comparable performance with normal KD from a superior teacher, which is well applied when a stronger teacher model is unavailable. Meanwhile, Tf-KD is generic and can be directly deployed for training deep neural networks. Without any extra computation cost, Tf-KD achieves up to 0.65% improvement on ImageNet over well-established baseline models, which is superior to label smoothing regularization. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yuan_Revisiting_Knowledge_Distillation_via_Label_Smoothing_Regularization_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yuan_Revisiting_Knowledge_Distillation_via_Label_Smoothing_Regularization_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yuan_Revisiting_Knowledge_Distillation_via_Label_Smoothing_Regularization_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yuan_Revisiting_Knowledge_Distillation_CVPR_2020_supplemental.pdf | null | null |
Modeling Biological Immunity to Adversarial Examples | Edward Kim, Jocelyn Rego, Yijing Watkins, Garrett T. Kenyon | While deep learning continues to permeate through all fields of signal processing and machine learning, a critical exploit in these frameworks exists and remains unsolved. These exploits, or adversarial examples, are a type of signal attack that can change the output class of a classifier by perturbing the stimulus signal by an imperceptible amount. The attack takes advantage of statistical irregularities within the training data, where the added perturbations can move the image across deep learning decision boundaries. What is even more alarming is the transferability of these attacks to different deep learning models and architectures. This means a successful attack on one model has adversarial effects on other, unrelated models. In a general sense, adversarial attack through perturbations is not a machine learning vulnerability. Human and biological vision can also be fooled by various methods, i.e. mixing high and low frequency images together, by altering semantically related signals, or by sufficiently distorting the input signal. However, the amount and magnitude of such a distortion required to alter biological perception is at a much larger scale. In this work, we explored this gap through the lens of biology and neuroscience in order to understand the robustness exhibited in human perception. Our experiments show that by leveraging sparsity and modeling the biological mechanisms at a cellular level, we are able to mitigate the effect of adversarial alterations to the signal that have no perceptible meaning. Furthermore, we present and illustrate the effects of top-down functional processes that contribute to the inherent immunity in human perception in the context of exploiting these properties to make a more robust machine vision system. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kim_Modeling_Biological_Immunity_to_Adversarial_Examples_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Modeling_Biological_Immunity_to_Adversarial_Examples_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Modeling_Biological_Immunity_to_Adversarial_Examples_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Rethinking Differentiable Search for Mixed-Precision Neural Networks | Zhaowei Cai, Nuno Vasconcelos | Low-precision networks, with weights and activations quantized to low bit-width, are widely used to accelerate inference on edge devices. However, current solutions are uniform, using identical bit-width for all filters. This fails to account for the different sensitivities of different filters and is suboptimal. Mixed-precision networks address this problem, by tuning the bit-width to individual filter requirements. In this work, the problem of optimal mixed-precision network search (MPS) is considered. To circumvent its difficulties of discrete search space and combinatorial optimization, a new differentiable search architecture is proposed, with several novel contributions to advance the efficiency by leveraging the unique properties of the MPS problem. The resulting Efficient differentiable MIxed-Precision network Search (EdMIPS) method is effective at finding the optimal bit allocation for multiple popular networks, and can search a large model, e.g. Inception-V3, directly on ImageNet without proxy task in a reasonable amount of time. The learned mixed-precision networks significantly outperform their uniform counterparts. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cai_Rethinking_Differentiable_Search_for_Mixed-Precision_Neural_Networks_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.05795 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cai_Rethinking_Differentiable_Search_for_Mixed-Precision_Neural_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cai_Rethinking_Differentiable_Search_for_Mixed-Precision_Neural_Networks_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Wavelet Synthesis Net for Disparity Estimation to Synthesize DSLR Calibre Bokeh Effect on Smartphones | Chenchi Luo, Yingmao Li, Kaimo Lin, George Chen, Seok-Jun Lee, Jihwan Choi, Youngjun Francis Yoo, Michael O. Polley | Modern smartphone cameras can match traditional DSLR cameras in many areas thanks to the introduction of camera arrays and multi-frame processing. Among all types of DSLR effects, the narrow depth of field (DoF) or so called bokeh probably arouses most interest. Today's smartphones try to overcome the physical lens and sensor limitations by introducing computational methods that utilize a depth map to synthesize the narrow DoF effect from all-in-focus images. However, a high quality depth map remains to be the key differentiator between computational bokeh and DSLR optical bokeh. Empowered by a novel wavelet synthesis network architecture, we have greatly narrowed the gap between DSLR and smartphone camera in terms of the bokeh more than ever before. We describe three key Modern smartphone cameras can match traditional digital single lens reflex (DSLR) cameras in many areas thanks to the introduction of camera arrays and multi-frame processing. Among all types of DSLR effects, the narrow depth of field (DoF) or so called bokeh probably arouses most interest. Today's smartphones try to overcome the physical lens and sensor limitations by introducing computational methods that utilize a depth map to synthesize the narrow DoF effect from all-in-focus images. However, a high quality depth map remains to be the key differentiator between computational bokeh and DSLR optical bokeh. Empowered by a novel wavelet synthesis network architecture, we have narrowed the gap between DSLR and smartphone camera in terms of bokeh more than ever before. We describe three key enablers of our bokeh solution: a synthetic graphics engine to generate training data with precisely prescribed characteristics that match the real smartphone captures, a novel wavelet synthesis neural network (WSN) architecture to produce unprecedented high definition disparity map promptly on smartphones, and a new evaluation metric to quantify the quality of the disparity map for real images from the bokeh rendering perspective. Experimental results show that the disparity map produced from our neural network achieves much better accuracy than the other state-of-the-art CNN based algorithms. Combining the high resolution disparity map with our rendering algorithm, we demonstrate visually superior bokeh pictures compared with existing top rated flagship smartphones listed on the DXOMARK mobiles. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Luo_Wavelet_Synthesis_Net_for_Disparity_Estimation_to_Synthesize_DSLR_Calibre_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=tkDazJlKGlU | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Luo_Wavelet_Synthesis_Net_for_Disparity_Estimation_to_Synthesize_DSLR_Calibre_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Luo_Wavelet_Synthesis_Net_for_Disparity_Estimation_to_Synthesize_DSLR_Calibre_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Luo_Wavelet_Synthesis_Net_CVPR_2020_supplemental.pdf | null | null |
Structure-Guided Ranking Loss for Single Image Depth Prediction | Ke Xian, Jianming Zhang, Oliver Wang, Long Mai, Zhe Lin, Zhiguo Cao | Single image depth prediction is a challenging task due to its ill-posed nature and challenges with capturing ground truth for supervision. Large-scale disparity data generated from stereo photos and 3D videos is a promising source of supervision, however, such disparity data can only approximate the inverse ground truth depth up to an affine transformation. To more effectively learn from such pseudo-depth data, we propose to use a simple pair-wise ranking loss with a novel sampling strategy. Instead of randomly sampling point pairs, we guide the sampling to better characterize structure of important regions based on the low-level edge maps and high-level object instance masks. We show that the pair-wise ranking loss, combined with our structure-guided sampling strategies, can significantly improve the quality of depth map prediction. In addition, we introduce a new relative depth dataset of about 21K diverse high-resolution web stereo photos to enhance the generalization ability of our model. In experiments, we conduct cross-dataset evaluation on six benchmark datasets and show that our method consistently improves over the baselines, leading to superior quantitative and qualitative results. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xian_Structure-Guided_Ranking_Loss_for_Single_Image_Depth_Prediction_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xian_Structure-Guided_Ranking_Loss_for_Single_Image_Depth_Prediction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xian_Structure-Guided_Ranking_Loss_for_Single_Image_Depth_Prediction_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Xian_Structure-Guided_Ranking_Loss_CVPR_2020_supplemental.pdf | null | null |
Perspective Plane Program Induction From a Single Image | Yikai Li, Jiayuan Mao, Xiuming Zhang, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu | We study the inverse graphics problem of inferring a holistic representation for natural images. Given an input image, our goal is to induce a neuro-symbolic, program-like representation that jointly models camera poses, object locations, and global scene structures. Such high-level, holistic scene representations further facilitate low-level image manipulation tasks such as inpainting. We formulate this problem as jointly finding the camera pose and scene structure that best describe the input image. The benefits of such joint inference are two-fold: scene regularity serves as a new cue for perspective correction, and in turn, correct perspective correction leads to a simplified scene structure, similar to how the correct shape leads to the most regular texture in shape from texture. Our proposed framework, Perspective Plane Program Induction (P3I), combines search-based and gradient-based algorithms to efficiently solve the problem. P3I outperforms a set of baselines on a collection of Internet images, across tasks including camera pose estimation, global structure inference, and down-stream image manipulation tasks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Perspective_Plane_Program_Induction_From_a_Single_Image_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.14708 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Perspective_Plane_Program_Induction_From_a_Single_Image_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Perspective_Plane_Program_Induction_From_a_Single_Image_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Perspective_Plane_Program_CVPR_2020_supplemental.pdf | null | null |
ActionBytes: Learning From Trimmed Videos to Localize Actions | Mihir Jain, Amir Ghodrati, Cees G. M. Snoek | This paper tackles the problem of localizing actions in long untrimmed videos. Different from existing works, which all use annotated untrimmed videos during training, we learn only from short trimmed videos. This enables learning from large-scale datasets originally designed for action classification. We propose a method to train an action localization network that segments a video into interpretable fragments, we call ActionBytes. Our method jointly learns to cluster ActionBytes and trains the localization network using the cluster assignments as pseudo-labels. By doing so, we train on short trimmed videos that become untrimmed for ActionBytes. In isolation, or when merged, the ActionBytes also serve as effective action proposals. Experiments demonstrate that our boundary-guided training generalizes to unknown action classes and localizes actions in long videos of Thumos14, MultiThumos, and ActivityNet1.2. Furthermore, we show the advantage of ActionBytes for zero-shot localization as well as traditional weakly supervised localization, that train on long videos, to achieve state-of-the-art results. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jain_ActionBytes_Learning_From_Trimmed_Videos_to_Localize_Actions_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jain_ActionBytes_Learning_From_Trimmed_Videos_to_Localize_Actions_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jain_ActionBytes_Learning_From_Trimmed_Videos_to_Localize_Actions_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Conv-MPN: Convolutional Message Passing Neural Network for Structured Outdoor Architecture Reconstruction | Fuyang Zhang, Nelson Nauata, Yasutaka Furukawa | This paper proposes a novel message passing neural (MPN) architecture Conv-MPN, which reconstructs an outdoor building as a planar graph from a single RGB image. Conv-MPN is specifically designed for cases where nodes of a graph have explicit spatial embedding. In our problem, nodes correspond to building edges in an image. Conv-MPN is different from MPN in that 1) the feature associated with a node is represented as a feature volume instead of a 1D vector; and 2) convolutions encode messages instead of fully connected layers. Conv-MPN learns to select a true subset of nodes (i.e., building edges) to reconstruct a building planar graph. Our qualitative and quantitative evaluations over 2,000 buildings show that Conv-MPN makes significant improvements over the existing fully neural solutions. We believe that the paper has a potential to open a new line of graph neural network research for structured geometry reconstruction. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Conv-MPN_Convolutional_Message_Passing_Neural_Network_for_Structured_Outdoor_Architecture_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=zUX3IL_t7jI | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Conv-MPN_Convolutional_Message_Passing_Neural_Network_for_Structured_Outdoor_Architecture_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Conv-MPN_Convolutional_Message_Passing_Neural_Network_for_Structured_Outdoor_Architecture_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhang_Conv-MPN_Convolutional_Message_CVPR_2020_supplemental.pdf | null | null |
Novel Object Viewpoint Estimation Through Reconstruction Alignment | Mohamed El Banani, Jason J. Corso, David F. Fouhey | The goal of this paper is to estimate the viewpoint for a novel object. Standard viewpoint estimation approaches generally fail on this task due to their reliance on a 3D model for alignment or large amounts of class-specific training data and their corresponding canonical pose. We overcome those limitations by learning a reconstruct and align approach. Our key insight is that although we do not have an explicit 3D model or a predefined canonical pose, we can still learn to estimate the object's shape in the viewer's frame and then use an image to provide our reference model or canonical pose. In particular, we propose learning two networks: the first maps images to a 3D geometry-aware feature bottleneck and is trained via an image-to-image translation loss; the second learns whether two instances of features are aligned. At test time, our model finds the relative transformation that best aligns the bottleneck features of our test image to a reference image. We evaluate our method on novel object viewpoint estimation by generalizing across different datasets, analyzing the impact of our different modules, and providing a qualitative analysis of the learned features to identify what representation is being learnt for alignment. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Banani_Novel_Object_Viewpoint_Estimation_Through_Reconstruction_Alignment_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.03586 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Banani_Novel_Object_Viewpoint_Estimation_Through_Reconstruction_Alignment_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Banani_Novel_Object_Viewpoint_Estimation_Through_Reconstruction_Alignment_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Banani_Novel_Object_Viewpoint_CVPR_2020_supplemental.pdf | null | null |
PaStaNet: Toward Human Activity Knowledge Engine | Yong-Lu Li, Liang Xu, Xinpeng Liu, Xijie Huang, Yue Xu, Shiyi Wang, Hao-Shu Fang, Ze Ma, Mingyang Chen, Cewu Lu | Existing image-based activity understanding methods mainly adopt direct mapping, i.e. from image to activity concepts, which may encounter performance bottleneck since the huge gap. In light of this, we propose a new path: infer human part states first and then reason out the activities based on part-level semantics. Human Body Part States (PaSta) are fine-grained action semantic tokens, e.g. , which can compose the activities and help us step toward human activity knowledge engine. To fully utilize the power of PaSta, we build a large-scale knowledge base PaStaNet, which contains 7M+ PaSta annotations. And two corresponding models are proposed: first, we design a model named Activity2Vec to extract PaSta features, which aim to be general representations for various activities. Second, we use a PaSta-based Reasoning method to infer activities. Promoted by PaStaNet, our method achieves significant improvements, e.g. 6.4 and 13.9 mAP on full and one-shot sets of HICO in supervised learning, and 3.2 and 4.2 mAP on V-COCO and images-based AVA in transfer learning. Code and data are available at http://hake-mvig.cn/. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_PaStaNet_Toward_Human_Activity_Knowledge_Engine_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00945 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_PaStaNet_Toward_Human_Activity_Knowledge_Engine_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_PaStaNet_Toward_Human_Activity_Knowledge_Engine_CVPR_2020_paper.html | CVPR 2020 | null | https://cove.thecvf.com/datasets/319 | null |
Dynamic Fluid Surface Reconstruction Using Deep Neural Network | Simron Thapa, Nianyi Li, Jinwei Ye | Recovering the dynamic fluid surface is a long-standing challenging problem in computer vision. Most existing image-based methods require multiple views or a dedicated imaging system. Here we present a learning-based single-image approach for 3D fluid surface reconstruction. Specifically, we design a deep neural network that estimates the depth and normal maps of a fluid surface by analyzing the refractive distortion of a reference background image. Due to the dynamic nature of fluid surfaces, our network uses recurrent layers that carry temporal information from previous frames to achieve spatio-temporally consistent reconstruction given a video input. Due to the lack of fluid data, we synthesize a large fluid dataset using physics-based fluid modeling and rendering techniques for network training and validation. Through experiments on simulated and real captured fluid images, we demonstrate that our proposed deep neural network trained on our fluid dataset can recover dynamic 3D fluid surfaces with high accuracy. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Thapa_Dynamic_Fluid_Surface_Reconstruction_Using_Deep_Neural_Network_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Thapa_Dynamic_Fluid_Surface_Reconstruction_Using_Deep_Neural_Network_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Thapa_Dynamic_Fluid_Surface_Reconstruction_Using_Deep_Neural_Network_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Thapa_Dynamic_Fluid_Surface_CVPR_2020_supplemental.pdf | null | null |
MPM: Joint Representation of Motion and Position Map for Cell Tracking | Junya Hayashida, Kazuya Nishimura, Ryoma Bise | Conventional cell tracking methods detect multiple cells in each frame (detection) and then associate the detection results in successive time-frames (association). Most cell tracking methods perform the association task independently from the detection task. However, there is no guarantee of preserving coherence between these tasks, and lack of coherence may adversely affect tracking performance. In this paper, we propose the Motion and Position Map (MPM) that jointly represents both detection and association for not only migration but also cell division. It guarantees coherence such that if a cell is detected, the corresponding motion flow can always be obtained. It is a simple but powerful method for multi-object tracking in dense environments. We compared the proposed method with current tracking methods under various conditions in real biological images and found that it outperformed the state-of-the-art (+5.2% improvement compared to the second-best). | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hayashida_MPM_Joint_Representation_of_Motion_and_Position_Map_for_Cell_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.10749 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hayashida_MPM_Joint_Representation_of_Motion_and_Position_Map_for_Cell_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hayashida_MPM_Joint_Representation_of_Motion_and_Position_Map_for_Cell_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
AdderNet: Do We Really Need Multiplications in Deep Learning? | Hanting Chen, Yunhe Wang, Chunjing Xu, Boxin Shi, Chao Xu, Qi Tian, Chang Xu | Compared with cheap addition operation, multiplication operation is of much higher computation complexity. The widely-used convolutions in deep neural networks are exactly cross-correlation to measure the similarity between input feature and convolution filters, which involves massive multiplications between float values. In this paper, we present adder networks (AdderNets) to trade these massive multiplications in deep neural networks, especially convolutional neural networks (CNNs), for much cheaper additions to reduce computation costs. In AdderNets, we take the L1-norm distance between filters and input feature as the output response. The influence of this new similarity measure on the optimization of neural network have been thoroughly analyzed. To achieve a better performance, we develop a special back-propagation approach for AdderNets by investigating the full-precision gradient. We then propose an adaptive learning rate strategy to enhance the training procedure of AdderNets according to the magnitude of each neuron's gradient. As a result, the proposed AdderNets can achieve 74.9% Top-1 accuracy 91.7% Top-5 accuracy using ResNet-50 on the ImageNet dataset without any multiplication in convolutional layer. The codes are publicly available at: (https://github.com/huaweinoah/AdderNet). | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_AdderNet_Do_We_Really_Need_Multiplications_in_Deep_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.13200 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_AdderNet_Do_We_Really_Need_Multiplications_in_Deep_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_AdderNet_Do_We_Really_Need_Multiplications_in_Deep_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_AdderNet_Do_We_CVPR_2020_supplemental.pdf | null | null |
Adaptive Interaction Modeling via Graph Operations Search | Haoxin Li, Wei-Shi Zheng, Yu Tao, Haifeng Hu, Jian-Huang Lai | Interaction modeling is important for video action analysis. Recently, several works design specific structures to model interactions in videos. However, their structures are manually designed and non-adaptive, which require structures design efforts and more importantly could not model interactions adaptively. In this paper, we automate the process of structures design to learn adaptive structures for interaction modeling. We propose to search the network structures with differentiable architecture search mechanism, which learns to construct adaptive structures for different videos to facilitate adaptive interaction modeling. To this end, we first design the search space with several basic graph operations that explicitly capture different relations in videos. We experimentally demonstrate that our architecture search framework learns to construct adaptive interaction modeling structures, which provides more understanding about the relations between the structures and some interaction characteristics, and also releases the requirement of structures design efforts. Additionally, we show that the designed basic graph operations in the search space are able to model different interactions in videos. The experiments on two interaction datasets show that our method achieves competitive performance with state-of-the-arts. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Adaptive_Interaction_Modeling_via_Graph_Operations_Search_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.02113 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Adaptive_Interaction_Modeling_via_Graph_Operations_Search_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Adaptive_Interaction_Modeling_via_Graph_Operations_Search_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Adaptive_Interaction_Modeling_CVPR_2020_supplemental.zip | null | null |
Differentiable Volumetric Rendering: Learning Implicit 3D Representations Without 3D Supervision | Michael Niemeyer, Lars Mescheder, Michael Oechsle, Andreas Geiger | Learning-based 3D reconstruction methods have shown impressive results. However, most methods require 3D supervision which is often hard to obtain for real-world datasets. Recently, several works have proposed differentiable rendering techniques to train reconstruction models from RGB images. Unfortunately, these approaches are currently restricted to voxel- and mesh-based representations, suffering from discretization or low resolution. In this work, we propose a differentiable rendering formulation for implicit shape and texture representations. Implicit representations have recently gained popularity as they represent shape and texture continuously. Our key insight is that depth gradients can be derived analytically using the concept of implicit differentiation. This allows us to learn implicit shape and texture representations directly from RGB images. We experimentally show that our single-view reconstructions rival those learned with full 3D supervision. Moreover, we find that our method can be used for multi-view 3D reconstruction, directly resulting in watertight meshes. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Niemeyer_Differentiable_Volumetric_Rendering_Learning_Implicit_3D_Representations_Without_3D_Supervision_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.07372 | https://www.youtube.com/watch?v=hbo7f76rTYs | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Niemeyer_Differentiable_Volumetric_Rendering_Learning_Implicit_3D_Representations_Without_3D_Supervision_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Niemeyer_Differentiable_Volumetric_Rendering_Learning_Implicit_3D_Representations_Without_3D_Supervision_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising | Haokui Zhang, Ying Li, Hao Chen, Chunhua Shen | Recently, neural architecture search (NAS) methods have attracted much attention and outperformed manually designed architectures on a few high-level vision tasks. In this paper, we propose HiNAS (Hierarchical NAS), an effort towards employing NAS to automatically design effective neural network architectures for image denoising. HiNAS adopts gradient based search strategies and employs operations with adaptive receptive field to build an flexible hierarchical search space. During the search stage, HiNAS shares cells across different feature levels to save memory and employ an early stopping strategy to avoid the collapse issue in NAS, and considerably accelerate the search speed. The proposed HiNAS is both memory and computation efficient, which takes only about 4.5 hours for searching using a single GPU. We evaluate the effectiveness of our proposed HiNAS on two different datasets, namely an additive white Gaussian noise dataset BSD500, and a realistic noise datasetSIM1800. Experimental results show that the architecture found by HiNAS has fewer parameters and enjoys a faster inference speed, while achieving highly competitive performance compared with state-of-the-art methods. We also present analysis on the architectures found by NAS. HiNAS also shows good performance on experiments for image de-raining. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Memory-Efficient_Hierarchical_Neural_Architecture_Search_for_Image_Denoising_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.08228 | https://www.youtube.com/watch?v=fRzOtwBl7Eg | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Memory-Efficient_Hierarchical_Neural_Architecture_Search_for_Image_Denoising_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Memory-Efficient_Hierarchical_Neural_Architecture_Search_for_Image_Denoising_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhang_Memory-Efficient_Hierarchical_Neural_CVPR_2020_supplemental.pdf | null | null |
Embodied Language Grounding With 3D Visual Feature Representations | Mihir Prabhudesai, Hsiao-Yu Fish Tung, Syed Ashar Javed, Maximilian Sieb, Adam W. Harley, Katerina Fragkiadaki | We propose associating language utterances to 3D visual abstractions of the scene they describe. The 3D visual abstractions are encoded as 3-dimensional visual feature maps. We infer these 3D visual scene feature maps from RGB images of the scene via view prediction: when the generated 3D scene feature map is neurally projected from a camera viewpoint, it should match the corresponding RGB image. We present generative models that condition on the dependency tree of an utterance and generate a corresponding visual 3D feature map as well as reason about its plausibility, and detector models that condition on both the dependency tree of an utterance and a related image and localize the object referents in the 3D feature map inferred from the image. Our model outperforms models of language and vision that associate language with 2D CNN activations or 2D images by a large margin in a variety of tasks, such as, classifying plausibility of utterances, detecting referential expressions, and supplying rewards for trajectory optimization of object placement policies from language instructions. We perform numerous ablations and show the improved performance of our detectors is due to its better generalization across camera viewpoints and lack of object interferences in the inferred 3D feature space, and the improved performance of our generators is due to their ability to spatially reason about objects and their configurations in 3D when mapping from language to scenes. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Prabhudesai_Embodied_Language_Grounding_With_3D_Visual_Feature_Representations_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Prabhudesai_Embodied_Language_Grounding_With_3D_Visual_Feature_Representations_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Prabhudesai_Embodied_Language_Grounding_With_3D_Visual_Feature_Representations_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Prabhudesai_Embodied_Language_Grounding_CVPR_2020_supplemental.pdf | null | null |
Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching | Xiaodong Gu, Zhiwen Fan, Siyu Zhu, Zuozhuo Dai, Feitong Tan, Ping Tan | The deep multi-view stereo (MVS) and stereo matching approaches generally construct 3D cost volumes to regularize and regress the output depth or disparity. These methods are limited when high-resolution outputs are needed since the memory and time costs grow cubically as the volume resolution increases. In this paper, we propose a both memory and time efficient cost volume formulation that is complementary to existing multi-view stereo and stereo matching approaches based on 3D cost volumes. First, the proposed cost volume is built upon a standard feature pyramid encoding geometry and context at gradually finer scales. Then, we can narrow the depth (or disparity) range of each stage by the depth (or disparity) map from the previous stage. With gradually higher cost volume resolution and adaptive adjustment of depth (or disparity) intervals, the output is recovered in a coarser to fine manner. We apply the cascade cost volume to the representative MVS-Net, and obtain a 35.6% improvement on DTU benchmark (1st place), with 50.6% and 59.3% reduction in GPU memory and run-time. It is also the state-of-the-art learning-based method on Tanks and Temples benchmark. The statistics of accuracy, run-time and GPU memory on other representative stereo CNNs also validate the effectiveness of our proposed method. Our source code is available at https://github.com/alibaba/cascade-stereo. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gu_Cascade_Cost_Volume_for_High-Resolution_Multi-View_Stereo_and_Stereo_Matching_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.06378 | https://www.youtube.com/watch?v=rcJiRQqDKbo | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gu_Cascade_Cost_Volume_for_High-Resolution_Multi-View_Stereo_and_Stereo_Matching_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gu_Cascade_Cost_Volume_for_High-Resolution_Multi-View_Stereo_and_Stereo_Matching_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
All in One Bad Weather Removal Using Architectural Search | Ruoteng Li, Robby T. Tan, Loong-Fah Cheong | Many methods have set state-of-the-art performance on restoring images degraded by bad weather such as rain, haze, fog, and snow, however they are designed specifically to handle one type of degradation. In this paper, we propose a method that can handle multiple bad weather degradations: rain, fog, snow and adherent raindrops using a single network. To achieve this, we first design a generator with multiple task-specific encoders, each of which is associated with a particular bad weather degradation type. We utilize a neural architecture search to optimally process the image features extracted from all encoders. Subsequently, to convert degraded image features to clean background features, we introduce a series of tensor-based operations encapsulating the underlying physics principles behind the formation of rain, fog, snow and adherent raindrops. These operations serve as the basic building blocks for our architectural search. Finally, our discriminator simultaneously assesses the correctness and classifies the degradation type of the restored image. We design a novel adversarial learning scheme that only backpropagates the loss of a degradation type to the respective task-specific encoder. Despite being designed to handle different types of bad weather, extensive experiments demonstrate that our method performs competitively to the individual and dedicated state-of-the-art image restoration methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_All_in_One_Bad_Weather_Removal_Using_Architectural_Search_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_All_in_One_Bad_Weather_Removal_Using_Architectural_Search_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_All_in_One_Bad_Weather_Removal_Using_Architectural_Search_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement | Zehao Yu, Shenghua Gao | Almost all previous deep learning-based multi-view stereo (MVS) approaches focus on improving reconstruction quality. Besides quality, efficiency is also a desirable feature for MVS in real scenarios. Towards this end, this paper presents a Fast-MVSNet, a novel sparse-to-dense coarse-to-fine framework, for fast and accurate depth estimation in MVS. Specifically, in our Fast-MVSNet, we first construct a sparse cost volume for learning a sparse and high-resolution depth map. Then we leverage a small-scale convolutional neural network to encode the depth dependencies for pixels within a local region to densify the sparse high-resolution depth map. At last, a simple but efficient Gauss-Newton layer is proposed to further optimize the depth map. On one hand, the high-resolution depth map, the data-adaptive propagation method and the Gauss-Newton layer jointly guarantee the effectiveness of our method. On the other hand, all modules in our Fast-MVSNet are lightweight and thus guarantee the efficiency of our approach. Besides, our approach is also memory-friendly because of the sparse depth representation. Extensive experimental results show that our method is 5 times and 14 times faster than Point-MVSNet and R-MVSNet, respectively, while achieving comparable or even better results on the challenging Tanks and Temples dataset as well as the DTU dataset. Code is available at https://github.com/svip-lab/FastMVSNet. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yu_Fast-MVSNet_Sparse-to-Dense_Multi-View_Stereo_With_Learned_Propagation_and_Gauss-Newton_Refinement_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=34c3Vf9g7kM | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_Fast-MVSNet_Sparse-to-Dense_Multi-View_Stereo_With_Learned_Propagation_and_Gauss-Newton_Refinement_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_Fast-MVSNet_Sparse-to-Dense_Multi-View_Stereo_With_Learned_Propagation_and_Gauss-Newton_Refinement_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yu_Fast-MVSNet_Sparse-to-Dense_Multi-View_CVPR_2020_supplemental.pdf | null | null |
Auxiliary Training: Towards Accurate and Robust Models | Linfeng Zhang, Muzhou Yu, Tong Chen, Zuoqiang Shi, Chenglong Bao, Kaisheng Ma | Training process is crucial for the deployment of the network in applications which have two strict requirements on both accuracy and robustness. However, most existing approaches are in a dilemma, i.e. model accuracy and robustness form an embarrassing tradeoff - the improvement of one leads to the drop of the other. The challenge remains as for we try to improve the accuracy and robustness simultaneously. In this paper, we propose a novel training method via introducing the auxiliary classifiers for training on corrupted samples, while the clean samples are normally trained with the primary classifier. In the training stage, a novel distillation method named input-aware self distillation is proposed to facilitate the primary classifier to learn the robust information from auxiliary classifiers. Along with it, a new normalization method - selective batch normalization is proposed to prevent the model from the negative influence of corrupted images. At the end of training period, a L2-norm penalty is applied to the weights of primary and auxiliary classifiers such that their weights are asymptotically identical. In the stage of inference, only the primary classifier is used and thus no extra computation and storage are needed. Extensive experiments on CIFAR10, CIFAR100 and ImageNet show that noticeable improvements on both accuracy and robustness can be observed by the proposed auxiliary training. On average, auxiliary training achieves 2.21% accuracy and 21.64% robustness (measured by corruption error) improvements over traditional training methods on CIFAR100. Codes has been released on github. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Auxiliary_Training_Towards_Accurate_and_Robust_Models_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Auxiliary_Training_Towards_Accurate_and_Robust_Models_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Auxiliary_Training_Towards_Accurate_and_Robust_Models_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Cascaded Human-Object Interaction Recognition | Tianfei Zhou, Wenguan Wang, Siyuan Qi, Haibin Ling, Jianbing Shen | Rapid progress has been witnessed for human-object interaction (HOI) recognition, but most existing models are confined to single-stage reasoning pipelines. Considering the intrinsic complexity of the task, we introduce a cascade architecture for a multi-stage, coarse-to-fine HOI understanding. At each stage, an instance localization network progressively refines HOI proposals and feeds them into an interaction recognition network. Each of the two networks is also connected to its predecessor at the previous stage, enabling cross-stage information propagation. The interaction recognition network has two crucial parts: a relation ranking module for high-quality HOI proposal selection and a triple-stream classifier for relation prediction. With our carefully-designed human-centric relation features, these two modules work collaboratively towards effective interaction understanding. Further beyond relation detection on a bounding-box level, we make our framework flexible to perform fine-grained pixel-wise relation segmentation; this provides a new glimpse into better relation modeling. Our approach reached the 1^ st place in the ICCV2019 Person in Context Challenge, on both relation detection and segmentation tasks. It also shows promising results on V-COCO. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhou_Cascaded_Human-Object_Interaction_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.04262 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Cascaded_Human-Object_Interaction_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Cascaded_Human-Object_Interaction_Recognition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Holistically-Attracted Wireframe Parsing | Nan Xue, Tianfu Wu, Song Bai, Fudong Wang, Gui-Song Xia, Liangpei Zhang, Philip H.S. Torr | This paper presents a fast and parsimonious parsing method to accurately and robustly detect a vectorized wireframe in an input image with a single forward pass. The proposed method is end-to-end trainable, consisting of three components: (i) line segment and junction proposal generation, (ii) line segment and junction matching, and (iii) line segment and junction verification. For computing line segment proposals, a novel exact dual representation is proposed which exploits a parsimonious geometric reparameterization for line segments and forms a holistic 4-dimensional attraction field map for an input image. Junctions can be treated as the "basins" in the attraction field. The proposed method is thus called Holistically-Attracted Wireframe Parser (HAWP). In experiments, the proposed method is tested on two benchmarks, the Wireframe dataset [14] and the YorkUrban dataset [8]. On both benchmarks, it obtains state-of-the-art performance in terms of accuracy and efficiency. For example, on the Wireframe dataset, compared to the previous state-of-the-art method L-CNN [36], it improves the challenging mean structural average precision (msAP) by a large margin (2.8% absolute improvements), and achieves 29.5 FPS on a single GPU (89% relative improvement). A systematic ablation study is performed to further justify the proposed method. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xue_Holistically-Attracted_Wireframe_Parsing_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.01663 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xue_Holistically-Attracted_Wireframe_Parsing_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xue_Holistically-Attracted_Wireframe_Parsing_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Strip Pooling: Rethinking Spatial Pooling for Scene Parsing | Qibin Hou, Li Zhang, Ming-Ming Cheng, Jiashi Feng | Spatial pooling has been proven highly effective to capture long-range contextual information for pixel-wise prediction tasks, such as scene parsing. In this paper, beyond conventional spatial pooling that usually has a regular shape of NxN, we rethink the formulation of spatial pooling by introducing a new pooling strategy, called strip pooling, which considers a long but narrow kernel, i.e., 1xN or Nx1. Based on strip pooling, we further investigate spatial pooling architecture design by 1) introducing a new strip pooling module that enables backbone networks to efficiently model long-range dependencies; 2) presenting a novel building block with diverse spatial pooling as a core; and 3) systematically comparing the performance of the proposed strip pooling and conventional spatial pooling techniques. Both novel pooling-based designs are lightweight and can serve as an efficient plug-and-play modules in existing scene parsing networks. Extensive experiments on Cityscapes and ADE20K benchmarks demonstrate that our simple approach establishes new state-of-the-art results. Code is available at https://github.com/Andrew-Qibin/SPNet. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hou_Strip_Pooling_Rethinking_Spatial_Pooling_for_Scene_Parsing_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13328 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hou_Strip_Pooling_Rethinking_Spatial_Pooling_for_Scene_Parsing_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hou_Strip_Pooling_Rethinking_Spatial_Pooling_for_Scene_Parsing_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
OASIS: A Large-Scale Dataset for Single Image 3D in the Wild | Weifeng Chen, Shengyi Qian, David Fan, Noriyuki Kojima, Max Hamilton, Jia Deng | Single-view 3D is the task of recovering 3D properties such as depth and surface normals from a single image. We hypothesize that a major obstacle to single-image 3D is data. We address this issue by presenting Open Annotations of Single Image Surfaces (OASIS), a dataset for single-image 3D in the wild consisting of annotations of detailed 3D geometry for 140,000 images. We train and evaluate leading models on a variety of single-image 3D tasks. We expect OASIS to be a useful resource for 3D vision research. Project site: https://pvl.cs.princeton.edu/OASIS. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_OASIS_A_Large-Scale_Dataset_for_Single_Image_3D_in_the_CVPR_2020_paper.pdf | http://arxiv.org/abs/2007.13215 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_OASIS_A_Large-Scale_Dataset_for_Single_Image_3D_in_the_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_OASIS_A_Large-Scale_Dataset_for_Single_Image_3D_in_the_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_OASIS_A_Large-Scale_CVPR_2020_supplemental.pdf | null | null |
CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus | Florian Kluger, Eric Brachmann, Hanno Ackermann, Carsten Rother, Michael Ying Yang, Bodo Rosenhahn | We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements. Applications include finding multiple vanishing points in man-made scenes, fitting planes to architectural imagery, or estimating multiple rigid motions within the same sequence. In contrast to previous works, which resorted to hand-crafted search strategies for multiple model detection, we learn the search strategy from data. A neural network conditioned on previously detected models guides a RANSAC estimator to different subsets of all measurements, thereby finding model instances one after another. We train our method supervised, as well as, self-supervised. For supervised training of the search strategy, we contribute a new dataset for vanishing point estimation. Leveraging this dataset, the proposed algorithm is superior with respect to other robust estimators, as well as, to designated vanishing point estimation algorithms. For self-supervised learning of the search, we evaluate the proposed algorithm on multi-homography estimation and demonstrate an accuracy that is superior to state-of-the-art methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kluger_CONSAC_Robust_Multi-Model_Fitting_by_Conditional_Sample_Consensus_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.02643 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kluger_CONSAC_Robust_Multi-Model_Fitting_by_Conditional_Sample_Consensus_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kluger_CONSAC_Robust_Multi-Model_Fitting_by_Conditional_Sample_Consensus_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kluger_CONSAC_Robust_Multi-Model_CVPR_2020_supplemental.pdf | https://cove.thecvf.com/datasets/315 | null |
Deep Global Registration | Christopher Choy, Wei Dong, Vladlen Koltun | We present Deep Global Registration, a differentiable framework for pairwise registration of real-world 3D scans. Deep global registration is based on three modules: a 6-dimensional convolutional network for correspondence confidence prediction, a differentiable Weighted Procrustes algorithm for closed-form pose estimation, and a robust gradient-based SE(3) optimizer for pose refinement. Experiments demonstrate that our approach outperforms state-of-the-art methods, both learning-based and classical, on real-world data. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Choy_Deep_Global_Registration_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.11540 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Choy_Deep_Global_Registration_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Choy_Deep_Global_Registration_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Choy_Deep_Global_Registration_CVPR_2020_supplemental.pdf | null | null |
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