Dataset Viewer
Search is not available for this dataset
title
string | authors
string | abstract
string | pdf
string | arXiv
string | video
string | bibtex
string | url
string | detail_url
string | tags
string | supp
string | dataset
null | null |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
Dual Super-Resolution Learning for Semantic Segmentation | Li Wang, Dong Li, Yousong Zhu, Lu Tian, Yi Shan | Current state-of-the-art semantic segmentation methods often apply high-resolution input to attain high performance, which brings large computation budgets and limits their applications on resource-constrained devices. In this paper, we propose a simple and flexible two-stream framework named Dual Super-Resolution Learning (DSRL) to effectively improve the segmentation accuracy without introducing extra computation costs. Specifically, the proposed method consists of three parts: Semantic Segmentation Super-Resolution (SSSR), Single Image Super-Resolution (SISR) and Feature Affinity (FA) module, which can keep high-resolution representations with low-resolution input while simultaneously reducing the model computation complexity. Moreover, it can be easily generalized to other tasks, e.g., human pose estimation. This simple yet effective method leads to strong representations and is evidenced by promising performance on both semantic segmentation and human pose estimation. Specifically, for semantic segmentation on CityScapes, we can achieve \geq2% higher mIoU with similar FLOPs, and keep the performance with 70% FLOPs. For human pose estimation, we can gain \geq2% mAP with the same FLOPs and maintain mAP with 30% fewer FLOPs. Code and models are available at https://github.com/wanglixilinx/DSRL. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Dual_Super-Resolution_Learning_for_Semantic_Segmentation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Dual_Super-Resolution_Learning_for_Semantic_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Dual_Super-Resolution_Learning_for_Semantic_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Deep Unfolding Network for Image Super-Resolution | Kai Zhang, Luc Van Gool, Radu Timofte | Learning-based single image super-resolution (SISR) methods are continuously showing superior effectiveness and efficiency over traditional model-based methods, largely due to the end-to-end training. However, different from model-based methods that can handle the SISR problem with different scale factors, blur kernels and noise levels under a unified MAP (maximum a posteriori) framework, learning-based methods generally lack such flexibility. To address this issue, this paper proposes an end-to-end trainable unfolding network which leverages both learningbased methods and model-based methods. Specifically, by unfolding the MAP inference via a half-quadratic splitting algorithm, a fixed number of iterations consisting of alternately solving a data subproblem and a prior subproblem can be obtained. The two subproblems then can be solved with neural modules, resulting in an end-to-end trainable, iterative network. As a result, the proposed network inherits the flexibility of model-based methods to super-resolve blurry, noisy images for different scale factors via a single model, while maintaining the advantages of learning-based methods. Extensive experiments demonstrate the superiority of the proposed deep unfolding network in terms of flexibility, effectiveness and also generalizability. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Deep_Unfolding_Network_for_Image_Super-Resolution_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.10428 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Deep_Unfolding_Network_for_Image_Super-Resolution_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Deep_Unfolding_Network_for_Image_Super-Resolution_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Unsupervised Learning for Intrinsic Image Decomposition From a Single Image | Yunfei Liu, Yu Li, Shaodi You, Feng Lu | Intrinsic image decomposition, which is an essential task in computer vision, aims to infer the reflectance and shading of the scene. It is challenging since it needs to separate one image into two components. To tackle this, conventional methods introduce various priors to constrain the solution, yet with limited performance. Meanwhile, the problem is typically solved by supervised learning methods, which is actually not an ideal solution since obtaining ground truth reflectance and shading for massive general natural scenes is challenging and even impossible. In this paper, we propose a novel unsupervised intrinsic image decomposition framework, which relies on neither labeled training data nor hand-crafted priors. Instead, it directly learns the latent feature of reflectance and shading from unsupervised and uncorrelated data. To enable this, we explore the independence between reflectance and shading, the domain invariant content constraint and the physical constraint. Extensive experiments on both synthetic and real image datasets demonstrate consistently superior performance of the proposed method. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Unsupervised_Learning_for_Intrinsic_Image_Decomposition_From_a_Single_Image_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.09930 | https://www.youtube.com/watch?v=qGszWVyDF9c | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Unsupervised_Learning_for_Intrinsic_Image_Decomposition_From_a_Single_Image_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Unsupervised_Learning_for_Intrinsic_Image_Decomposition_From_a_Single_Image_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Liu_Unsupervised_Learning_for_CVPR_2020_supplemental.pdf | null | null |
COCAS: A Large-Scale Clothes Changing Person Dataset for Re-Identification | Shijie Yu, Shihua Li, Dapeng Chen, Rui Zhao, Junjie Yan, Yu Qiao | Recent years have witnessed great progress in person re-identification (re-id). Several academic benchmarks such as Market1501, CUHK03 and DukeMTMC play important roles to promote the re-id research. To our best knowledge, all the existing benchmarks assume the same person will have the same clothes. While in real-world scenarios, it is very often for a person to change clothes. To address the clothes changing person re-id problem, we construct a novel large-scale re-id benchmark named Clothes Changing Person Set (COCAS), which provides multiple images of the same identity with different clothes. COCAS totally contains 62,382 body images from 5,266 persons. Based on COCAS, we introduce a new person re-id setting for clothes changing problem, where the query includes both a clothes template and a person image taking another clothes. Moreover, we propose a two-branch network named Biometric-Clothes Network (BC-Net) which can effectively integrate biometric and clothes feature for re-id under our setting. Experiments show that it is feasible for clothes changing re-id with clothes templates. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yu_COCAS_A_Large-Scale_Clothes_Changing_Person_Dataset_for_Re-Identification_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.07862 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_COCAS_A_Large-Scale_Clothes_Changing_Person_Dataset_for_Re-Identification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_COCAS_A_Large-Scale_Clothes_Changing_Person_Dataset_for_Re-Identification_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Dynamic Convolutions: Exploiting Spatial Sparsity for Faster Inference | Thomas Verelst, Tinne Tuytelaars | Modern convolutional neural networks apply the same operations on every pixel in an image. However, not all image regions are equally important. To address this inefficiency, we propose a method to dynamically apply convolutions conditioned on the input image. We introduce a residual block where a small gating branch learns which spatial positions should be evaluated. These discrete gating decisions are trained end-to-end using the Gumbel-Softmax trick, in combination with a sparsity criterion. Our experiments on CIFAR, ImageNet, Food-101 and MPII show that our method has better focus on the region of interest and better accuracy than existing methods, at a lower computational complexity. Moreover, we provide an efficient CUDA implementation of our dynamic convolutions using a gather-scatter approach, achieving a significant improvement in inference speed on MobileNetV2 and ShuffleNetV2. On human pose estimation, a task that is inherently spatially sparse, the processing speed is increased by 60% with no loss in accuracy. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Verelst_Dynamic_Convolutions_Exploiting_Spatial_Sparsity_for_Faster_Inference_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.03203 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Verelst_Dynamic_Convolutions_Exploiting_Spatial_Sparsity_for_Faster_Inference_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Verelst_Dynamic_Convolutions_Exploiting_Spatial_Sparsity_for_Faster_Inference_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Verelst_Dynamic_Convolutions_Exploiting_CVPR_2020_supplemental.pdf | null | null |
Alleviation of Gradient Exploding in GANs: Fake Can Be Real | Song Tao, Jia Wang | In order to alleviate the notorious mode collapse phenomenon in generative adversarial networks (GANs), we propose a novel training method of GANs in which certain fake samples are considered as real ones during the training process. This strategy can reduce the gradient value that generator receives in the region where gradient exploding happens. We show the process of an unbalanced generation and a vicious circle issue resulted from gradient exploding in practical training, which explains the instability of GANs. We also theoretically prove that gradient exploding can be alleviated by penalizing the difference between discriminator outputs and fake-as-real consideration for very close real and fake samples. Accordingly, Fake-As-Real GAN (FARGAN) is proposed with a more stable training process and a more faithful generated distribution. Experiments on different datasets verify our theoretical analysis. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tao_Alleviation_of_Gradient_Exploding_in_GANs_Fake_Can_Be_Real_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.12485 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tao_Alleviation_of_Gradient_Exploding_in_GANs_Fake_Can_Be_Real_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tao_Alleviation_of_Gradient_Exploding_in_GANs_Fake_Can_Be_Real_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Tao_Alleviation_of_Gradient_CVPR_2020_supplemental.pdf | null | null |
Forward and Backward Information Retention for Accurate Binary Neural Networks | Haotong Qin, Ruihao Gong, Xianglong Liu, Mingzhu Shen, Ziran Wei, Fengwei Yu, Jingkuan Song | Weight and activation binarization is an effective approach to deep neural network compression and can accelerate the inference by leveraging bitwise operations. Although many binarization methods have improved the accuracy of the model by minimizing the quantization error in forward propagation, there remains a noticeable performance gap between the binarized model and the full-precision one. Our empirical study indicates that the quantization brings information loss in both forward and backward propagation, which is the bottleneck of training accurate binary neural networks. To address these issues, we propose an Information Retention Network (IR-Net) to retain the information that consists in the forward activations and backward gradients. IR-Net mainly relies on two technical contributions: (1) Libra Parameter Binarization (Libra-PB): simultaneously minimizing both quantization error and information loss of parameters by balanced and standardized weights in forward propagation; (2) Error Decay Estimator (EDE): minimizing the information loss of gradients by gradually approximating the sign function in backward propagation, jointly considering the updating ability and accurate gradients. We are the first to investigate both forward and backward processes of binary networks from the unified information perspective, which provides new insight into the mechanism of network binarization. Comprehensive experiments with various network structures on CIFAR-10 and ImageNet datasets manifest that the proposed IR-Net can consistently outperform state-of-the-art quantization methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Qin_Forward_and_Backward_Information_Retention_for_Accurate_Binary_Neural_Networks_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.10788 | https://www.youtube.com/watch?v=EsbwQTDWeXA | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Qin_Forward_and_Backward_Information_Retention_for_Accurate_Binary_Neural_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Qin_Forward_and_Backward_Information_Retention_for_Accurate_Binary_Neural_Networks_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Cooling-Shrinking Attack: Blinding the Tracker With Imperceptible Noises | Bin Yan, Dong Wang, Huchuan Lu, Xiaoyun Yang | Adversarial attack of CNN aims at deceiving models to misbehave by adding imperceptible perturbations to images. This feature facilitates to understand neural networks deeply and to improve the robustness of deep learning models. Although several works have focused on attacking image classifiers and object detectors, an effective and efficient method for attacking single object trackers of any target in a model-free way remains lacking. In this paper, a cooling-shrinking attack method is proposed to deceive state-of-the-art SiameseRPN-based trackers. An effective and efficient perturbation generator is trained with a carefully designed adversarial loss, which can simultaneously cool hot regions where the target exists on the heatmaps and force the predicted bounding box to shrink, making the tracked target invisible to trackers. Numerous experiments on OTB100, VOT2018, and LaSOT datasets show that our method can effectively fool the state-of-the-art SiameseRPN++ tracker by adding small perturbations to the template or the search regions. Besides, our method has good transferability and is able to deceive other top-performance trackers such as DaSiamRPN, DaSiamRPN-UpdateNet, and DiMP. The source codes are available at https://github.com/MasterBin-IIAU/CSA. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yan_Cooling-Shrinking_Attack_Blinding_the_Tracker_With_Imperceptible_Noises_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.09595 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yan_Cooling-Shrinking_Attack_Blinding_the_Tracker_With_Imperceptible_Noises_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yan_Cooling-Shrinking_Attack_Blinding_the_Tracker_With_Imperceptible_Noises_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution | Xiaoyu Xiang, Yapeng Tian, Yulun Zhang, Yun Fu, Jan P. Allebach, Chenliang Xu | In this paper, we explore the space-time video super-resolution task, which aims to generate a high-resolution (HR) slow-motion video from a low frame rate (LFR), low-resolution (LR) video. A simple solution is to split it into two sub-tasks: video frame interpolation (VFI) and video super-resolution (VSR). However, temporal interpolation and spatial super-resolution are intra-related in this task. Two-stage methods cannot fully take advantage of the natural property. In addition, state-of-the-art VFI or VSR networks require a large frame-synthesis or reconstruction module for predicting high-quality video frames, which makes the two-stage methods have large model sizes and thus be time-consuming. To overcome the problems, we propose a one-stage space-time video super-resolution framework, which directly synthesizes an HR slow-motion video from an LFR, LR video. Rather than synthesizing missing LR video frames as VFI networks do, we firstly temporally interpolate LR frame features in missing LR video frames capturing local temporal contexts by the proposed feature temporal interpolation network. Then, we propose a deformable ConvLSTM to align and aggregate temporal information simultaneously for better leveraging global temporal contexts. Finally, a deep reconstruction network is adopted to predict HR slow-motion video frames. Extensive experiments on benchmark datasets demonstrate that the proposed method not only achieves better quantitative and qualitative performance but also is more than three times faster than recent two-stage state-of-the-art methods, e.g., DAIN+EDVR and DAIN+RBPN. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xiang_Zooming_Slow-Mo_Fast_and_Accurate_One-Stage_Space-Time_Video_Super-Resolution_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=5NrIHdicyAo | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xiang_Zooming_Slow-Mo_Fast_and_Accurate_One-Stage_Space-Time_Video_Super-Resolution_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xiang_Zooming_Slow-Mo_Fast_and_Accurate_One-Stage_Space-Time_Video_Super-Resolution_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Xiang_Zooming_Slow-Mo_Fast_CVPR_2020_supplemental.pdf | null | null |
A Hierarchical Graph Network for 3D Object Detection on Point Clouds | Jintai Chen, Biwen Lei, Qingyu Song, Haochao Ying, Danny Z. Chen, Jian Wu | 3D object detection on point clouds finds many applications. However, most known point cloud object detection methods did not adequately accommodate the characteristics (e.g., sparsity) of point clouds, and thus some key semantic information (e.g., shape information) is not well captured. In this paper, we propose a new graph convolution (GConv) based hierarchical graph network (HGNet) for 3D object detection, which processes raw point clouds directly to predict 3D bounding boxes. HGNet effectively captures the relationship of the points and utilizes the multi-level semantics for object detection. Specially, we propose a novel shape-attentive GConv (SA-GConv) to capture the local shape features, by modelling the relative geometric positions of points to describe object shapes. An SA-GConv based U-shape network captures the multi-level features, which are mapped into an identical feature space by an improved voting module and then further utilized to generate proposals. Next, a new GConv based Proposal Reasoning Module reasons on the proposals considering the global scene semantics, and the bounding boxes are then predicted. Consequently, our new framework outperforms state-of-the-art methods on two large-scale point cloud datasets, by 4% mean average precision (mAP) on SUN RGB-D and by 3% mAP on ScanNet-V2. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_A_Hierarchical_Graph_Network_for_3D_Object_Detection_on_Point_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_A_Hierarchical_Graph_Network_for_3D_Object_Detection_on_Point_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_A_Hierarchical_Graph_Network_for_3D_Object_Detection_on_Point_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Online Joint Multi-Metric Adaptation From Frequent Sharing-Subset Mining for Person Re-Identification | Jiahuan Zhou, Bing Su, Ying Wu | Person Re-IDentification (P-RID), as an instance-level recognition problem, still remains challenging in computer vision community. Many P-RID works aim to learn faithful and discriminative features/metrics from offline training data and directly use them for the unseen online testing data. However, their performance is largely limited due to the severe data shifting issue between training and testing data. Therefore, we propose an online joint multi-metric adaptation model to adapt the offline learned P-RID models for the online data by learning a series of metrics for all the sharing-subsets. Each sharing-subset is obtained from the proposed novel frequent sharing-subset mining module and contains a group of testing samples which share strong visual similarity relationships to each other. Unlike existing online P-RID methods, our model simultaneously takes both the sample-specific discriminant and the set-based visual similarity among testing samples into consideration so that the adapted multiple metrics can refine the discriminant of all the given testing samples jointly via a multi-kernel late fusion framework. Our proposed model is generally suitable to any offline learned P-RID baselines for online boosting, the performance improvement by our model is not only verified by extensive experiments on several widely-used P-RID benchmarks (CUHK03, Market1501, DukeMTMC-reID and MSMT17) and state-of-the-art P-RID baselines but also guaranteed by the provided in-depth theoretical analyses. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhou_Online_Joint_Multi-Metric_Adaptation_From_Frequent_Sharing-Subset_Mining_for_Person_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=Q38l8pKqNsc | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Online_Joint_Multi-Metric_Adaptation_From_Frequent_Sharing-Subset_Mining_for_Person_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Online_Joint_Multi-Metric_Adaptation_From_Frequent_Sharing-Subset_Mining_for_Person_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning to Discriminate Information for Online Action Detection | Hyunjun Eun, Jinyoung Moon, Jongyoul Park, Chanho Jung, Changick Kim | From a streaming video, online action detection aims to identify actions in the present. For this task, previous methods use recurrent networks to model the temporal sequence of current action frames. However, these methods overlook the fact that an input image sequence includes background and irrelevant actions as well as the action of interest. For online action detection, in this paper, we propose a novel recurrent unit to explicitly discriminate the information relevant to an ongoing action from others. Our unit, named Information Discrimination Unit (IDU), decides whether to accumulate input information based on its relevance to the current action. This enables our recurrent network with IDU to learn a more discriminative representation for identifying ongoing actions. In experiments on two benchmark datasets, TVSeries and THUMOS-14, the proposed method outperforms state-of-the-art methods by a significant margin. Moreover, we demonstrate the effectiveness of our recurrent unit by conducting comprehensive ablation studies. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Eun_Learning_to_Discriminate_Information_for_Online_Action_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.04461 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Eun_Learning_to_Discriminate_Information_for_Online_Action_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Eun_Learning_to_Discriminate_Information_for_Online_Action_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Video to Events: Recycling Video Datasets for Event Cameras | Daniel Gehrig, Mathias Gehrig, Javier Hidalgo-Carrio, Davide Scaramuzza | Event cameras are novel sensors that output brightness changes in the form of a stream of asynchronous "events" instead of intensity frames. They offer significant advantages with respect to conventional cameras: high dynamic range (HDR), high temporal resolution, and no motion blur. Recently, novel learning approaches operating on event data have achieved impressive results. Yet, these methods require a large amount of event data for training, which is hardly available due the novelty of event sensors in computer vision research. In this paper, we present a method that addresses these needs by converting any existing video dataset recorded with conventional cameras to synthetic event data. This unlocks the use of a virtually unlimited number of existing video datasets for training networks designed for real event data. We evaluate our method on two relevant vision tasks, i.e., object recognition and semantic segmentation, and show that models trained on synthetic events have several benefits: (i) they generalize well to real event data, even in scenarios where standard-camera images are blurry or overexposed, by inheriting the outstanding properties of event cameras; (ii) they can be used for fine-tuning on real data to improve over state-of-the-art for both classification and semantic segmentation. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gehrig_Video_to_Events_Recycling_Video_Datasets_for_Event_Cameras_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gehrig_Video_to_Events_Recycling_Video_Datasets_for_Event_Cameras_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gehrig_Video_to_Events_Recycling_Video_Datasets_for_Event_Cameras_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Gehrig_Video_to_Events_CVPR_2020_supplemental.zip | null | null |
Bundle Pooling for Polygonal Architecture Segmentation Problem | Huayi Zeng, Kevin Joseph, Adam Vest, Yasutaka Furukawa | This paper introduces a polygonal architecture segmentation problem, proposes bundle-pooling modules for line structure reasoning, and demonstrates a virtual remodeling application that produces production quality results. Given a photograph of a house with a few vanishing point candidates, we decompose the house into a set of architectural components, each of which is represented as a simple geometric primitive. A bundle-pooling module pools convolutional features along a bundle of line segments (e.g., a family of vanishing lines) and fuses the bundle of features to determine polygonal boundaries or assign a corresponding vanishing point. Qualitative and quantitative evaluations demonstrate significant improvements over the existing techniques based on our metric and benchmark dataset. We will share the code and data for further research. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zeng_Bundle_Pooling_for_Polygonal_Architecture_Segmentation_Problem_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zeng_Bundle_Pooling_for_Polygonal_Architecture_Segmentation_Problem_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zeng_Bundle_Pooling_for_Polygonal_Architecture_Segmentation_Problem_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Use the Force, Luke! Learning to Predict Physical Forces by Simulating Effects | Kiana Ehsani, Shubham Tulsiani, Saurabh Gupta, Ali Farhadi, Abhinav Gupta | When we humans look at a video of human-object interaction, we can not only infer what is happening but we can even extract actionable information and imitate those interactions. On the other hand, current recognition or geometric approaches lack the physicality of action representation. In this paper, we take a step towards more physical understanding of actions. We address the problem of inferring contact points and the physical forces from videos of humans interacting with objects. One of the main challenges in tackling this problem is obtaining ground-truth labels for forces. We sidestep this problem by instead using a physics simulator for supervision. Specifically, we use a simulator to predict effects, and enforce that estimated forces must lead to same effect as depicted in the video. Our quantitative and qualitative results show that (a) we can predict meaningful forces from videos whose effects lead to accurate imitation of the motions observed, (b) by jointly optimizing for contact point and force prediction, we can improve the performance on both tasks in comparison to independent training, and (c) we can learn a representation from this model that generalizes to novel objects using few shot examples. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ehsani_Use_the_Force_Luke_Learning_to_Predict_Physical_Forces_by_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12045 | https://www.youtube.com/watch?v=dx3_nXcOqV0 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ehsani_Use_the_Force_Luke_Learning_to_Predict_Physical_Forces_by_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ehsani_Use_the_Force_Luke_Learning_to_Predict_Physical_Forces_by_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Ehsani_Use_the_Force_CVPR_2020_supplemental.zip | null | null |
Articulation-Aware Canonical Surface Mapping | Nilesh Kulkarni, Abhinav Gupta, David F. Fouhey, Shubham Tulsiani | We tackle the tasks of: 1) predicting a Canonical Surface Mapping (CSM) that indicates the mapping from 2D pixels to corresponding points on a canonical template shape , and 2) inferring the articulation and pose of the template corresponding to the input image. While previous approaches rely on keypoint supervision for learning, we present an approach that can learn without such annotations. Our key insight is that these tasks are geometrically related, and we can obtain supervisory signal via enforcing consistency among the predictions. We present results across a diverse set of animal object categories, showing that our method can learn articulation and CSM prediction from image collections using only foreground mask labels for training. We empirically show that allowing articulation helps learn more accurate CSM prediction, and that enforcing the consistency with predicted CSM is similarly critical for learning meaningful articulation. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kulkarni_Articulation-Aware_Canonical_Surface_Mapping_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00614 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kulkarni_Articulation-Aware_Canonical_Surface_Mapping_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kulkarni_Articulation-Aware_Canonical_Surface_Mapping_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kulkarni_Articulation-Aware_Canonical_Surface_CVPR_2020_supplemental.pdf | null | null |
NeuralScale: Efficient Scaling of Neurons for Resource-Constrained Deep Neural Networks | Eugene Lee, Chen-Yi Lee | Deciding the amount of neurons during the design of a deep neural network to maximize performance is not intuitive. In this work, we attempt to search for the neuron (filter) configuration of a fixed network architecture that maximizes accuracy. Using iterative pruning methods as a proxy, we parametrize the change of the neuron (filter) number of each layer with respect to the change in parameters, allowing us to efficiently scale an architecture across arbitrary sizes. We also introduce architecture descent which iteratively refines the parametrized function used for model scaling. The combination of both proposed methods is coined as NeuralScale. To prove the efficiency of NeuralScale in terms of parameters, we show empirical simulations on VGG11, MobileNetV2 and ResNet18 using CIFAR10, CIFAR100 and TinyImageNet as benchmark datasets. Our results show an increase in accuracy of 3.04%, 8.56% and 3.41% for VGG11, MobileNetV2 and ResNet18 on CIFAR10, CIFAR100 and TinyImageNet respectively under a parameter-constrained setting (output neurons (filters) of default configuration with scaling factor of 0.25). | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lee_NeuralScale_Efficient_Scaling_of_Neurons_for_Resource-Constrained_Deep_Neural_Networks_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.12813 | https://www.youtube.com/watch?v=Se0cf-uk_L8 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_NeuralScale_Efficient_Scaling_of_Neurons_for_Resource-Constrained_Deep_Neural_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_NeuralScale_Efficient_Scaling_of_Neurons_for_Resource-Constrained_Deep_Neural_Networks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lee_NeuralScale_Efficient_Scaling_CVPR_2020_supplemental.pdf | null | null |
Transfer Learning From Synthetic to Real-Noise Denoising With Adaptive Instance Normalization | Yoonsik Kim, Jae Woong Soh, Gu Yong Park, Nam Ik Cho | Real-noise denoising is a challenging task because the statistics of real-noise do not follow the normal distribution, and they are also spatially and temporally changing. In order to cope with various and complex real-noise, we propose a well-generalized denoising architecture and a transfer learning scheme. Specifically, we adopt an adaptive instance normalization to build a denoiser, which can regularize the feature map and prevent the network from overfitting to the training set. We also introduce a transfer learning scheme that transfers knowledge learned from synthetic-noise data to the real-noise denoiser. From the proposed transfer learning, the synthetic-noise denoiser can learn general features from various synthetic-noise data, and the real-noise denoiser can learn the real-noise characteristics from real data. From the experiments, we find that the proposed denoising method has great generalization ability, such that our network trained with synthetic-noise achieves the best performance for Darmstadt Noise Dataset (DND) among the methods from published papers. We can also see that the proposed transfer learning scheme robustly works for real-noise images through the learning with a very small number of labeled data. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kim_Transfer_Learning_From_Synthetic_to_Real-Noise_Denoising_With_Adaptive_Instance_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.11244 | https://www.youtube.com/watch?v=qWnEkDE-oe8 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Transfer_Learning_From_Synthetic_to_Real-Noise_Denoising_With_Adaptive_Instance_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Transfer_Learning_From_Synthetic_to_Real-Noise_Denoising_With_Adaptive_Instance_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kim_Transfer_Learning_From_CVPR_2020_supplemental.pdf | null | null |
Variational Context-Deformable ConvNets for Indoor Scene Parsing | Zhitong Xiong, Yuan Yuan, Nianhui Guo, Qi Wang | Context information is critical for image semantic segmentation. Especially in indoor scenes, the large variation of object scales makes spatial-context an important factor for improving the segmentation performance. Thus, in this paper, we propose a novel variational context-deformable (VCD) module to learn adaptive receptive-field in a structured fashion. Different from standard ConvNets, which share fixed-size spatial context for all pixels, the VCD module learns a deformable spatial-context with the guidance of depth information: depth information provides clues for identifying real local neighborhoods. Specifically, adaptive Gaussian kernels are learned with the guidance of multimodal information. By multiplying the learned Gaussian kernel with standard convolution filters, the VCD module can aggregate flexible spatial context for each pixel during convolution. The main contributions of this work are as follows: 1) a novel VCD module is proposed, which exploits learnable Gaussian kernels to enable feature learning with structured adaptive-context; 2) variational Bayesian probabilistic modeling is introduced for the training of VCD module, which can make it continuous and more stable; 3) a perspective-aware guidance module is designed to take advantage of multi-modal information for RGB-D segmentation. We evaluate the proposed approach on three widely-used datasets, and the performance improvement has shown the effectiveness of the proposed method. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xiong_Variational_Context-Deformable_ConvNets_for_Indoor_Scene_Parsing_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xiong_Variational_Context-Deformable_ConvNets_for_Indoor_Scene_Parsing_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xiong_Variational_Context-Deformable_ConvNets_for_Indoor_Scene_Parsing_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Xiong_Variational_Context-Deformable_ConvNets_CVPR_2020_supplemental.pdf | null | null |
Augmenting Colonoscopy Using Extended and Directional CycleGAN for Lossy Image Translation | Shawn Mathew, Saad Nadeem, Sruti Kumari, Arie Kaufman | Colorectal cancer screening modalities, such as optical colonoscopy (OC) and virtual colonoscopy (VC), are critical for diagnosing and ultimately removing polyps (precursors for colon cancer). The non-invasive VC is normally used to inspect a 3D reconstructed colon (from computed tomography scans) for polyps and if found, the OC procedure is performed to physically traverse the colon via endoscope and remove these polyps. In this paper, we present a deep learning framework, Extended and Directional CycleGAN, for lossy unpaired image-to-image translation between OC and VC to augment OC video sequences with scale-consistent depth information from VC and VC with patient-specific textures, color and specular highlights from OC (e.g. for realistic polyp synthesis). Both OC and VC contain structural information, but it is obscured in OC by additional patient-specific texture and specular highlights, hence making the translation from OC to VC lossy. The existing CycleGAN approaches do not handle lossy transformations. To address this shortcoming, we introduce an extended cycle consistency loss, which compares the geometric structures from OC in the VC domain. This loss removes the need for the CycleGAN to embed OC information in the VC domain. To handle a stronger removal of the textures and lighting, a Directional Discriminator is introduced to differentiate the direction of translation (by creating paired information for the discriminator), as opposed to the standard CycleGAN which is direction-agnostic. Combining the extended cycle consistency loss and the Directional Discriminator, we show state-of-the-art results on scale-consistent depth inference for phantom, textured VC and for real polyp and normal colon video sequences. We also present results for realistic pendunculated and flat polyp synthesis from bumps introduced in 3D VC models. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Mathew_Augmenting_Colonoscopy_Using_Extended_and_Directional_CycleGAN_for_Lossy_Image_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12473 | https://www.youtube.com/watch?v=9JZdnwtsE6I | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Mathew_Augmenting_Colonoscopy_Using_Extended_and_Directional_CycleGAN_for_Lossy_Image_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Mathew_Augmenting_Colonoscopy_Using_Extended_and_Directional_CycleGAN_for_Lossy_Image_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Mathew_Augmenting_Colonoscopy_Using_CVPR_2020_supplemental.zip | null | null |
BANet: Bidirectional Aggregation Network With Occlusion Handling for Panoptic Segmentation | Yifeng Chen, Guangchen Lin, Songyuan Li, Omar Bourahla, Yiming Wu, Fangfang Wang, Junyi Feng, Mingliang Xu, Xi Li | Panoptic segmentation aims to perform instance segmentation for foreground instances and semantic segmentation for background stuff simultaneously. The typical top-down pipeline concentrates on two key issues: 1) how to effectively model the intrinsic interaction between semantic segmentation and instance segmentation, and 2) how to properly handle occlusion for panoptic segmentation. Intuitively, the complementarity between semantic segmentation and instance segmentation can be leveraged to improve the performance. Besides, we notice that using detection/mask scores is insufficient for resolving the occlusion problem. Motivated by these observations, we propose a novel deep panoptic segmentation scheme based on a bidirectional learning pipeline. Moreover, we introduce a plug-and-play occlusion handling algorithm to deal with the occlusion between different object instances. The experimental results on COCO panoptic benchmark validate the effectiveness of our proposed method. Codes will be released soon at https://github.com/Mooonside/BANet. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_BANet_Bidirectional_Aggregation_Network_With_Occlusion_Handling_for_Panoptic_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.14031 | https://www.youtube.com/watch?v=UocwJjwjeII | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_BANet_Bidirectional_Aggregation_Network_With_Occlusion_Handling_for_Panoptic_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_BANet_Bidirectional_Aggregation_Network_With_Occlusion_Handling_for_Panoptic_Segmentation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_BANet_Bidirectional_Aggregation_CVPR_2020_supplemental.zip | null | null |
C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation | Qihang Yu, Dong Yang, Holger Roth, Yutong Bai, Yixiao Zhang, Alan L. Yuille, Daguang Xu | 3D convolution neural networks (CNN) have been proved very successful in parsing organs or tumours in 3D medical images, but it remains sophisticated and time-consuming to choose or design proper 3D networks given different task contexts. Recently, Neural Architecture Search (NAS) is proposed to solve this problem by searching for the best network architecture automatically. However, the inconsistency between search stage and deployment stage often exists in NAS algorithms due to memory constraints and large search space, which could become more serious when applying NAS to some memory and time-consuming tasks, such as 3D medical image segmentation. In this paper, we propose a coarse-to-fine neural architecture search (C2FNAS) to automatically search a 3D segmentation network from scratch without inconsistency on network size or input size. Specifically, we divide the search procedure into two stages: 1) the coarse stage, where we search the macro-level topology of the network, i.e. how each convolution module is connected to other modules; 2) the fine stage, where we search at micro-level for operations in each cell based on previous searched macro-level topology. The coarse-to-fine manner divides the search procedure into two consecutive stages and meanwhile resolves the inconsistency. We evaluate our method on 10 public datasets from Medical Segmentation Decalthon (MSD) challenge, and achieve state-of-the-art performance with the network searched using one dataset, which demonstrates the effectiveness and generalization of our searched models. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yu_C2FNAS_Coarse-to-Fine_Neural_Architecture_Search_for_3D_Medical_Image_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.09628 | https://www.youtube.com/watch?v=fonR1Q5tvDU | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_C2FNAS_Coarse-to-Fine_Neural_Architecture_Search_for_3D_Medical_Image_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_C2FNAS_Coarse-to-Fine_Neural_Architecture_Search_for_3D_Medical_Image_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Seeing the World in a Bag of Chips | Jeong Joon Park, Aleksander Holynski, Steven M. Seitz | We address the dual problems of novel view synthesis and environment reconstruction from hand-held RGBD sensors. Our contributions include 1) modeling highly specular objects, 2) modeling inter-reflections and Fresnel effects, and 3) enabling surface light field reconstruction with the same input needed to reconstruct shape alone. In cases where scene surface has a strong mirror-like material component, we generate highly detailed environment images, revealing room composition, objects, people, buildings, and trees visible through windows. Our approach yields state of the art view synthesis techniques, operates on low dynamic range imagery, and is robust to geometric and calibration errors. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Park_Seeing_the_World_in_a_Bag_of_Chips_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.04642 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Park_Seeing_the_World_in_a_Bag_of_Chips_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Park_Seeing_the_World_in_a_Bag_of_Chips_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Park_Seeing_the_World_CVPR_2020_supplemental.pdf | null | null |
Cascaded Deep Video Deblurring Using Temporal Sharpness Prior | Jinshan Pan, Haoran Bai, Jinhui Tang | We present a simple and effective deep convolutional neural network (CNN) model for video deblurring. The proposed algorithm mainly consists of optical flow estimation from intermediate latent frames and latent frame restoration steps. It first develops a deep CNN model to estimate optical flow from intermediate latent frames and then restores the latent frames based on the estimated optical flow. To better explore the temporal information from videos, we develop a temporal sharpness prior to constrain the deep CNN model to help the latent frame restoration. We develop an effective cascaded training approach and jointly train the proposed CNN model in an end-to-end manner. We show that exploring the domain knowledge of video deblurring is able to make the deep CNN model more compact and efficient. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods on the benchmark datasets as well as real-world videos. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Pan_Cascaded_Deep_Video_Deblurring_Using_Temporal_Sharpness_Prior_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.02501 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Pan_Cascaded_Deep_Video_Deblurring_Using_Temporal_Sharpness_Prior_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Pan_Cascaded_Deep_Video_Deblurring_Using_Temporal_Sharpness_Prior_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Pan_Cascaded_Deep_Video_CVPR_2020_supplemental.pdf | null | null |
Reflection Scene Separation From a Single Image | Renjie Wan, Boxin Shi, Haoliang Li, Ling-Yu Duan, Alex C. Kot | For images taken through glass, existing methods focus on the restoration of the background scene by regarding the reflection components as noise. However, the scene reflected by glass surface also contains important information to be recovered, especially for the surveillance or criminal investigations. In this paper, instead of removing reflection components from the mixture image, we aim at recovering reflection scenes from the mixture image. We first propose a strategy to obtain such ground truth and its corresponding input images. Then, we propose a two-stage framework to obtain the visible reflection scene from the mixture image. Specifically, we train the network with a shift-invariant loss which is robust to misalignment between the input and output images. The experimental results show that our proposed method achieves promising results. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wan_Reflection_Scene_Separation_From_a_Single_Image_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wan_Reflection_Scene_Separation_From_a_Single_Image_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wan_Reflection_Scene_Separation_From_a_Single_Image_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
SmallBigNet: Integrating Core and Contextual Views for Video Classification | Xianhang Li, Yali Wang, Zhipeng Zhou, Yu Qiao | Temporal convolution has been widely used for video classification. However, it is performed on spatio-temporal contexts in a limited view, which often weakens its capacity of learning video representation. To alleviate this problem, we propose a concise and novel SmallBig network, with the cooperation of small and big views. For the current time step, the small view branch is used to learn the core semantics, while the big view branch is used to capture the contextual semantics. Unlike traditional temporal convolution, the big view branch can provide the small view branch with the most activated video features from a broader 3D receptive field. Via aggregating such big-view contexts, the small view branch can learn more robust and discriminative spatio-temporal representations for video classification. Furthermore, we propose to share convolution in the small and big view branch, which improves model compactness as well as alleviates overfitting. As a result, our SmallBigNet achieves a comparable model size like 2D CNNs, while boosting accuracy like 3D CNNs. We conduct extensive experiments on the large-scale video benchmarks, e.g., Kinetics400, Something-Something V1 and V2. Our SmallBig network outperforms a number of recent state-of-the-art approaches, in terms of accuracy and/or efficiency. The codes and models will be available on https://github.com/xhl-video/SmallBigNet. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_SmallBigNet_Integrating_Core_and_Contextual_Views_for_Video_Classification_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.14582 | https://www.youtube.com/watch?v=JIj2VTzmgmM | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_SmallBigNet_Integrating_Core_and_Contextual_Views_for_Video_Classification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_SmallBigNet_Integrating_Core_and_Contextual_Views_for_Video_Classification_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
From Two Rolling Shutters to One Global Shutter | Cenek Albl, Zuzana Kukelova, Viktor Larsson, Michal Polic, Tomas Pajdla, Konrad Schindler | Most consumer cameras are equipped with electronic rolling shutter, leading to image distortions when the camera moves during image capture. We explore a surprisingly simple camera configuration that makes it possible to undo the rolling shutter distortion: two cameras mounted to have different rolling shutter directions. Such a setup is easy and cheap to build and it possesses the geometric constraints needed to correct rolling shutter distortion using only a sparse set of point correspondences between the two images. We derive equations that describe the underlying geometry for general and special motions and present an efficient method for finding their solutions. Our synthetic and real experiments demonstrate that our approach is able to remove large rolling shutter distortions of all types without relying on any specific scene structure. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Albl_From_Two_Rolling_Shutters_to_One_Global_Shutter_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.01964 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Albl_From_Two_Rolling_Shutters_to_One_Global_Shutter_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Albl_From_Two_Rolling_Shutters_to_One_Global_Shutter_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Albl_From_Two_Rolling_CVPR_2020_supplemental.pdf | null | null |
CvxNet: Learnable Convex Decomposition | Boyang Deng, Kyle Genova, Soroosh Yazdani, Sofien Bouaziz, Geoffrey Hinton, Andrea Tagliasacchi | Any solid object can be decomposed into a collection of convex polytopes (in short, convexes). When a small number of convexes are used, such a decomposition can be thought of as a piece-wise approximation of the geometry. This decomposition is fundamental in computer graphics, where it provides one of the most common ways to approximate geometry, for example, in real-time physics simulation. A convex object also has the property of being simultaneously an explicit and implicit representation: one can interpret it explicitly as a mesh derived by computing the vertices of a convex hull, or implicitly as the collection of half-space constraints or support functions. Their implicit representation makes them particularly well suited for neural network training, as they abstract away from the topology of the geometry they need to represent. However, at testing time, convexes can also generate explicit representations - polygonal meshes - which can then be used in any downstream application. We introduce a network architecture to represent a low dimensional family of convexes. This family is automatically derived via an auto-encoding process. We investigate the applications of this architecture including automatic convex decomposition, image to 3D reconstruction, and part-based shape retrieval. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Deng_CvxNet_Learnable_Convex_Decomposition_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.05736 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Deng_CvxNet_Learnable_Convex_Decomposition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Deng_CvxNet_Learnable_Convex_Decomposition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
RoboTHOR: An Open Simulation-to-Real Embodied AI Platform | Matt Deitke, Winson Han, Alvaro Herrasti, Aniruddha Kembhavi, Eric Kolve, Roozbeh Mottaghi, Jordi Salvador, Dustin Schwenk, Eli VanderBilt, Matthew Wallingford, Luca Weihs, Mark Yatskar, Ali Farhadi | Visual recognition ecosystems (e.g. ImageNet, Pascal, COCO) have undeniably played a prevailing role in the evolution of modern computer vision. We argue that interactive and embodied visual AI has reached a stage of development similar to visual recognition prior to the advent of these ecosystems. Recently, various synthetic environments have been introduced to facilitate research in embodied AI. Notwithstanding this progress, the crucial question of how well models trained in simulation generalize to reality has remained largely unanswered. The creation of a comparable ecosystem for simulation-to-real embodied AI presents many challenges: (1) the inherently interactive nature of the problem, (2) the need for tight alignments between real and simulated worlds, (3) the difficulty of replicating physical conditions for repeatable experiments, (4) and the associated cost. In this paper, we introduce RoboTHOR to democratize research in interactive and embodied visual AI. RoboTHOR offers a framework of simulated environments paired with physical counterparts to systematically explore and overcome the challenges of simulation-to-real transfer, and a platform where researchers across the globe can remotely test their embodied models in the physical world. As a first benchmark, our experiments show there exists a significant gap between the performance of models trained in simulation when they are tested in both simulations and their carefully constructed physical analogs. We hope that RoboTHOR will spur the next stage of evolution in embodied computer vision. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Deitke_RoboTHOR_An_Open_Simulation-to-Real_Embodied_AI_Platform_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.06799 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Deitke_RoboTHOR_An_Open_Simulation-to-Real_Embodied_AI_Platform_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Deitke_RoboTHOR_An_Open_Simulation-to-Real_Embodied_AI_Platform_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Style Normalization and Restitution for Generalizable Person Re-Identification | Xin Jin, Cuiling Lan, Wenjun Zeng, Zhibo Chen, Li Zhang | Existing fully-supervised person re-identification (ReID) methods usually suffer from poor generalization capability caused by domain gaps. The key to solving this problem lies in filtering out identity-irrelevant interference and learning domain-invariant person representations. In this paper, we aim to design a generalizable person ReID framework which trains a model on source domains yet is able to generalize/perform well on target domains. To achieve this goal, we propose a simple yet effective Style Normalization and Restitution (SNR) module. Specifically, we filter out style variations (e.g., illumination, color contrast) by Instance Normalization (IN). However, such a process inevitably removes discriminative information. We propose to distill identity-relevant feature from the removed information and restitute it to the network to ensure high discrimination. For better disentanglement, we enforce a dual causal loss constraint in SNR to encourage the separation of identity-relevant features and identity-irrelevant features. Extensive experiments demonstrate the strong generalization capability of our framework. Our models empowered by the SNR modules significantly outperform the state-of-the-art domain generalization approaches on multiple widely-used person ReID benchmarks, and also show superiority on unsupervised domain adaptation. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jin_Style_Normalization_and_Restitution_for_Generalizable_Person_Re-Identification_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.11037 | https://www.youtube.com/watch?v=BDd2hxpgznk | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jin_Style_Normalization_and_Restitution_for_Generalizable_Person_Re-Identification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jin_Style_Normalization_and_Restitution_for_Generalizable_Person_Re-Identification_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Jin_Style_Normalization_and_CVPR_2020_supplemental.pdf | null | null |
Training Noise-Robust Deep Neural Networks via Meta-Learning | Zhen Wang, Guosheng Hu, Qinghua Hu | Label noise may significantly degrade the performance of Deep Neural Networks (DNNs). To train noise-robust DNNs, Loss correction (LC) approaches have been introduced. LC approaches assume the noisy labels are corrupted from clean (ground-truth) labels by an unknown noise transition matrix T. The backbone DNNs and T can be trained separately, where T is approximated with prior knowledge. For example, T is constructed by stacking the maximum or mean predic- tions of the samples from each class. In this work, we pro- pose a new loss correction approach, named as Meta Loss Correction (MLC), to directly learn T from data via the meta-learning framework. The MLC is model-agnostic and learns T from data rather than heuristically approximates it using prior knowledge. Extensive evaluations are conducted on computer vision (MNIST, CIFAR-10, CIFAR-100, Cloth- ing1M) and natural language processing (Twitter) datasets. The experimental results show that MLC achieves very com- petitive performance against state-of-the-art approaches. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Training_Noise-Robust_Deep_Neural_Networks_via_Meta-Learning_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Training_Noise-Robust_Deep_Neural_Networks_via_Meta-Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Training_Noise-Robust_Deep_Neural_Networks_via_Meta-Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
HUMBI: A Large Multiview Dataset of Human Body Expressions | Zhixuan Yu, Jae Shin Yoon, In Kyu Lee, Prashanth Venkatesh, Jaesik Park, Jihun Yu, Hyun Soo Park | This paper presents a new large multiview dataset called HUMBI for human body expressions with natural clothing. The goal of HUMBI is to facilitate modeling view-specific appearance and geometry of gaze, face, hand, body, and garment from assorted people. 107 synchronized HD cam- eras are used to capture 772 distinctive subjects across gen- der, ethnicity, age, and physical condition. With the mul- tiview image streams, we reconstruct high fidelity body ex- pressions using 3D mesh models, which allows representing view-specific appearance using their canonical atlas. We demonstrate that HUMBI is highly effective in learning and reconstructing a complete human model and is complemen- tary to the existing datasets of human body expressions with limited views and subjects such as MPII-Gaze, Multi-PIE, Human3.6M, and Panoptic Studio datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yu_HUMBI_A_Large_Multiview_Dataset_of_Human_Body_Expressions_CVPR_2020_paper.pdf | http://arxiv.org/abs/1812.00281 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_HUMBI_A_Large_Multiview_Dataset_of_Human_Body_Expressions_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_HUMBI_A_Large_Multiview_Dataset_of_Human_Body_Expressions_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yu_HUMBI_A_Large_CVPR_2020_supplemental.zip | null | null |
Towards Transferable Targeted Attack | Maosen Li, Cheng Deng, Tengjiao Li, Junchi Yan, Xinbo Gao, Heng Huang | An intriguing property of adversarial examples is their transferability, which suggests that black-box attacks are feasible in real-world applications. Previous works mostly study the transferability on non-targeted setting. However, recent studies show that targeted adversarial examples are more difficult to transfer than non-targeted ones. In this paper, we find there exist two defects that lead to the difficulty in generating transferable examples. First, the magnitude of gradient is decreasing during iterative attack, causing excessive consistency between two successive noises in accumulation of momentum, which is termed as noise curing. Second, it is not enough for targeted adversarial examples to just get close to target class without moving away from true class. To overcome the above problems, we propose a novel targeted attack approach to effectively generate more transferable adversarial examples. Specifically, we first introduce the Poincare distance as the similarity metric to make the magnitude of gradient self-adaptive during iterative attack to alleviate noise curing. Furthermore, we regularize the targeted attack process with metric learning to take adversarial examples away from true label and gain more transferable targeted adversarial examples. Experiments on ImageNet validate the superiority of our approach achieving 8% higher attack success rate over other state-of-the-art methods on average in black-box targeted attack. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Towards_Transferable_Targeted_Attack_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Towards_Transferable_Targeted_Attack_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Towards_Transferable_Targeted_Attack_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Towards_Transferable_Targeted_CVPR_2020_supplemental.pdf | null | null |
Supervised Raw Video Denoising With a Benchmark Dataset on Dynamic Scenes | Huanjing Yue, Cong Cao, Lei Liao, Ronghe Chu, Jingyu Yang | In recent years, the supervised learning strategy for real noisy image denoising has been emerging and has achieved promising results. In contrast, realistic noise removal for raw noisy videos is rarely studied due to the lack of noisy-clean pairs for dynamic scenes. Clean video frames for dynamic scenes cannot be captured with a long-exposure shutter or averaging multi-shots as was done for static images. In this paper, we solve this problem by creating motions for controllable objects, such as toys, and capturing each static moment for multiple times to generate clean video frames. In this way, we construct a dataset with 55 groups of noisy-clean videos with ISO values ranging from 1600 to 25600. To our knowledge, this is the first dynamic video dataset with noisy-clean pairs. Correspondingly, we propose a raw video denoising network (RViDeNet) by exploring the temporal, spatial, and channel correlations of video frames. Since the raw video has Bayer patterns, we pack it into four sub-sequences, i.e RGBG sequences, which are denoised by the proposed RViDeNet separately and finally fused into a clean video. In addition, our network not only outputs a raw denoising result, but also the sRGB result by going through an image signal processing (ISP) module, which enables users to generate the sRGB result with their favourite ISPs. Experimental results demonstrate that our method outperforms state-of-the-art video and raw image denoising algorithms on both indoor and outdoor videos. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yue_Supervised_Raw_Video_Denoising_With_a_Benchmark_Dataset_on_Dynamic_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.14013 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yue_Supervised_Raw_Video_Denoising_With_a_Benchmark_Dataset_on_Dynamic_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yue_Supervised_Raw_Video_Denoising_With_a_Benchmark_Dataset_on_Dynamic_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yue_Supervised_Raw_Video_CVPR_2020_supplemental.pdf | null | null |
FDA: Fourier Domain Adaptation for Semantic Segmentation | Yanchao Yang, Stefano Soatto | We describe a simple method for unsupervised domain adaptation, whereby the discrepancy between the source and target distributions is reduced by swapping the low-frequency spectrum of one with the other. We illustrate the method in semantic segmentation, where densely annotated images are aplenty in one domain (synthetic data), but difficult to obtain in another (real images). Current state-of-the-art methods are complex, some requiring adversarial optimization to render the backbone of a neural network invariant to the discrete domain selection variable. Our method does not require any training to perform the domain alignment, just a simple Fourier Transform and its inverse. Despite its simplicity, it achieves state-of-the-art performance in the current benchmarks, when integrated into a relatively standard semantic segmentation model. Our results indicate that even simple procedures can discount nuisance variability in the data that more sophisticated methods struggle to learn away. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_FDA_Fourier_Domain_Adaptation_for_Semantic_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.05498 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_FDA_Fourier_Domain_Adaptation_for_Semantic_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_FDA_Fourier_Domain_Adaptation_for_Semantic_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
SGAS: Sequential Greedy Architecture Search | Guohao Li, Guocheng Qian, Itzel C. Delgadillo, Matthias Muller, Ali Thabet, Bernard Ghanem | Architecture design has become a crucial component of successful deep learning. Recent progress in automatic neural architecture search (NAS) shows a lot of promise. However, discovered architectures often fail to generalize in the final evaluation. Architectures with a higher validation accuracy during the search phase may perform worse in the evaluation. Aiming to alleviate this common issue, we introduce sequential greedy architecture search (SGAS), an efficient method for neural architecture search. By dividing the search procedure into sub-problems, SGAS chooses and prunes candidate operations in a greedy fashion. We apply SGAS to search architectures for Convolutional Neural Networks (CNN) and Graph Convolutional Networks (GCN). Extensive experiments show that SGAS is able to find state-of-the-art architectures for tasks such as image classification, point cloud classification and node classification in protein-protein interaction graphs with minimal computational cost. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_SGAS_Sequential_Greedy_Architecture_Search_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.00195 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_SGAS_Sequential_Greedy_Architecture_Search_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_SGAS_Sequential_Greedy_Architecture_Search_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_SGAS_Sequential_Greedy_CVPR_2020_supplemental.pdf | null | null |
Instance Segmentation of Biological Images Using Harmonic Embeddings | Victor Kulikov, Victor Lempitsky | We present a new instance segmentation approach tailored to biological images, where instances may correspond to individual cells, organisms or plant parts. Unlike instance segmentation for user photographs or road scenes, in biological data object instances may be particularly densely packed, the appearance variation may be particularly low, the processing power may be restricted, while, on the other hand, the variability of sizes of individual instances may be limited. The proposed approach successfully addresses these peculiarities. Our approach describes each object instance using an expectation of a limited number of sine waves with frequencies and phases adjusted to particular object sizes and densities. At train time, a fully-convolutional network is learned to predict the object embeddings at each pixel using a simple pixelwise regression loss, while at test time the instances are recovered using clustering in the embedding space. In the experiments, we show that our approach outperforms previous embedding-based instance segmentation approaches on a number of biological datasets, achieving state-of-the-art on a popular CVPPP benchmark. This excellent performance is combined with computational efficiency that is needed for deployment to domain specialists. The source code of the approach is available at https://github.com/kulikovv/harmonic . | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kulikov_Instance_Segmentation_of_Biological_Images_Using_Harmonic_Embeddings_CVPR_2020_paper.pdf | http://arxiv.org/abs/1904.05257 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kulikov_Instance_Segmentation_of_Biological_Images_Using_Harmonic_Embeddings_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kulikov_Instance_Segmentation_of_Biological_Images_Using_Harmonic_Embeddings_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Rethinking Zero-Shot Video Classification: End-to-End Training for Realistic Applications | Biagio Brattoli, Joseph Tighe, Fedor Zhdanov, Pietro Perona, Krzysztof Chalupka | Trained on large datasets, deep learning (DL) can accurately classify videos into hundreds of diverse classes. However, video data is expensive to annotate. Zero-shot learning (ZSL) proposes one solution to this problem. ZSL trains a model once, and generalizes to new tasks whose classes are not present in the training dataset. We propose the first end-to-end algorithm for ZSL in video classification. Our training procedure builds on insights from recent video classification literature and uses a trainable 3D CNN to learn the visual features. This is in contrast to previous video ZSL methods, which use pretrained feature extractors. We also extend the current benchmarking paradigm: Previous techniques aim to make the test task unknown at training time but fall short of this goal. We encourage domain shift across training and test data and disallow tailoring a ZSL model to a specific test dataset. We outperform the state-of-the-art by a wide margin. Our code, evaluation procedure and model weights are available online github.com/bbrattoli/ZeroShotVideoClassification. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Brattoli_Rethinking_Zero-Shot_Video_Classification_End-to-End_Training_for_Realistic_Applications_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.01455 | https://www.youtube.com/watch?v=F5AB06sCJ90 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Brattoli_Rethinking_Zero-Shot_Video_Classification_End-to-End_Training_for_Realistic_Applications_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Brattoli_Rethinking_Zero-Shot_Video_Classification_End-to-End_Training_for_Realistic_Applications_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Brattoli_Rethinking_Zero-Shot_Video_CVPR_2020_supplemental.pdf | null | null |
A Multigrid Method for Efficiently Training Video Models | Chao-Yuan Wu, Ross Girshick, Kaiming He, Christoph Feichtenhofer, Philipp Krahenbuhl | Training competitive deep video models is an order of magnitude slower than training their counterpart image models. Slow training causes long research cycles, which hinders progress in video understanding research. Following standard practice for training image models, video model training has used a fixed mini-batch shape: a specific number of clips, frames, and spatial size. However, what is the optimal shape? High resolution models perform well, but train slowly. Low resolution models train faster, but are less accurate. Inspired by multigrid methods in numerical optimization, we propose to use variable mini-batch shapes with different spatial-temporal resolutions that are varied according to a schedule. The different shapes arise from resampling the training data on multiple sampling grids. Training is accelerated by scaling up the mini-batch size and learning rate when shrinking the other dimensions. We empirically demonstrate a general and robust grid schedule that yields a significant out-of-the-box training speedup without a loss in accuracy for different models (I3D, non-local, SlowFast), datasets (Kinetics, Something-Something, Charades), and training settings (with and without pre-training, 128 GPUs or 1 GPU). As an illustrative example, the proposed multigrid method trains a ResNet-50 SlowFast network 4.5x faster (wall-clock time, same hardware) while also improving accuracy (+0.8% absolute) on Kinetics-400 compared to baseline training. Code is available online. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wu_A_Multigrid_Method_for_Efficiently_Training_Video_Models_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.00998 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_A_Multigrid_Method_for_Efficiently_Training_Video_Models_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_A_Multigrid_Method_for_Efficiently_Training_Video_Models_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wu_A_Multigrid_Method_CVPR_2020_supplemental.pdf | null | null |
Attention-Aware Multi-View Stereo | Keyang Luo, Tao Guan, Lili Ju, Yuesong Wang, Zhuo Chen, Yawei Luo | Multi-view stereo is a crucial task in computer vision, that requires accurate and robust photo-consistency among input images for depth estimation. Recent studies have shown that learning-based feature matching and confidence regularization can play a vital role in this task. Nevertheless, how to design good matching confidence volumes as well as effective regularizers for them are still under in-depth study. In this paper, we propose an attention-aware deep neural network "AttMVS" for learning multi-view stereo. In particular, we propose a novel attention-enhanced matching confidence volume, that combines the raw pixel-wise matching confidence from the extracted perceptual features with the contextual information of local scenes, to improve the matching robustness. Furthermore, we develop an attention-guided regularization module, which consists of multilevel ray fusion modules, to hierarchically aggregate and regularize the matching confidence volume into a latent depth probability volume.Experimental results show that our approach achieves the best overall performance on the DTU dataset and the intermediate sequences of Tanks & Temples benchmark over many state-of-the-art MVS algorithms. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Luo_Attention-Aware_Multi-View_Stereo_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Luo_Attention-Aware_Multi-View_Stereo_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Luo_Attention-Aware_Multi-View_Stereo_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
PPDM: Parallel Point Detection and Matching for Real-Time Human-Object Interaction Detection | Yue Liao, Si Liu, Fei Wang, Yanjie Chen, Chen Qian, Jiashi Feng | We propose a single-stage Human-Object Interaction (HOI) detection method that has outperformed all existing methods on HICO-DET dataset at 37 fps on a single Titan XP GPU. It is the first real-time HOI detection method. Conventional HOI detection methods are composed of two stages, i.e., human-object proposals generation, and proposals classification. Their effectiveness and efficiency are limited by the sequential and separate architecture. In this paper, we propose a Parallel Point Detection and Matching (PPDM) HOI detection framework. In PPDM, an HOI is defined as a point triplet < human point, interaction point, object point>. Human and object points are the center of the detection boxes, and the interaction point is the midpoint of the human and object points. PPDM contains two parallel branches, namely point detection branch and point matching branch. The point detection branch predicts three points. Simultaneously, the point matching branch predicts two displacements from the interaction point to its corresponding human and object points. The human point and the object point originated from the same interaction point are considered as matched pairs. In our novel parallel architecture, the interaction points implicitly provide context and regularization for human and object detection. The isolated detection boxes unlikely to form meaningful HOI triplets are suppressed, which increases the precision of HOI detection. Moreover, the matching between human and object detection boxes is only applied around limited numbers of filtered candidate interaction points, which saves much computational cost. Additionally, we build a new application-oriented database named HOI-A, which serves as a good supplement to the existing datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liao_PPDM_Parallel_Point_Detection_and_Matching_for_Real-Time_Human-Object_Interaction_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.12898 | https://www.youtube.com/watch?v=NxR-vtRIHNQ | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liao_PPDM_Parallel_Point_Detection_and_Matching_for_Real-Time_Human-Object_Interaction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liao_PPDM_Parallel_Point_Detection_and_Matching_for_Real-Time_Human-Object_Interaction_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models | Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, Cynthia Rudin | The primary aim of single-image super-resolution is to construct a high-resolution (HR) image from a corresponding low-resolution (LR) input. In previous approaches, which have generally been supervised, the training objective typically measures a pixel-wise average distance between the super-resolved (SR) and HR images. Optimizing such metrics often leads to blurring, especially in high variance (detailed) regions. We propose an alternative formulation of the super-resolution problem based on creating realistic SR images that downscale correctly. We present a novel super-resolution algorithm addressing this problem, PULSE (Photo Upsampling via Latent Space Exploration), which generates high-resolution, realistic images at resolutions previously unseen in the literature. It accomplishes this in an entirely self-supervised fashion and is not confined to a specific degradation operator used during training, unlike previous methods (which require training on databases of LR-HR image pairs for supervised learning). Instead of starting with the LR image and slowly adding detail, PULSE traverses the high-resolution natural image manifold, searching for images that downscale to the original LR image. This is formalized through the "downscaling loss," which guides exploration through the latent space of a generative model. By leveraging properties of high-dimensional Gaussians, we restrict the search space to guarantee that our outputs are realistic. PULSE thereby generates super-resolved images that both are realistic and downscale correctly. We show extensive experimental results demonstrating the efficacy of our approach in the domain of face super-resolution (also known as face hallucination). Our method outperforms state-of-the-art methods in perceptual quality at higher resolutions and scale factors than previously possible. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Menon_PULSE_Self-Supervised_Photo_Upsampling_via_Latent_Space_Exploration_of_Generative_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.03808 | https://www.youtube.com/watch?v=JCK-N4T_tMU | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Menon_PULSE_Self-Supervised_Photo_Upsampling_via_Latent_Space_Exploration_of_Generative_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Menon_PULSE_Self-Supervised_Photo_Upsampling_via_Latent_Space_Exploration_of_Generative_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Menon_PULSE_Self-Supervised_Photo_CVPR_2020_supplemental.pdf | null | null |
Discrete Model Compression With Resource Constraint for Deep Neural Networks | Shangqian Gao, Feihu Huang, Jian Pei, Heng Huang | In this paper, we target to address the problem of compression and acceleration of Convolutional Neural Networks (CNNs). Specifically, we propose a novel structural pruning method to obtain a compact CNN with strong discriminative power. To find such networks, we propose an efficient discrete optimization method to directly optimize channel-wise differentiable discrete gate under resource constraint while freezing all the other model parameters. Although directly optimizing discrete variables is a complex non-smooth, non-convex and NP-hard problem, our optimization method can circumvent these difficulties by using the straight-through estimator. Thus, our method is able to ensure that the sub-network discovered within the training process reflects the true sub-network. We further extend the discrete gate to its stochastic version in order to thoroughly explore the potential sub-networks. Unlike many previous methods requiring per-layer hyper-parameters, we only require one hyper-parameter to control FLOPs budget. Moreover, our method is globally discrimination-aware due to the discrete setting. The experimental results on CIFAR-10 and ImageNet show that our method is competitive with state-of-the-art methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gao_Discrete_Model_Compression_With_Resource_Constraint_for_Deep_Neural_Networks_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=2S2M3TJYSks | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_Discrete_Model_Compression_With_Resource_Constraint_for_Deep_Neural_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_Discrete_Model_Compression_With_Resource_Constraint_for_Deep_Neural_Networks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Gao_Discrete_Model_Compression_CVPR_2020_supplemental.pdf | null | null |
GhostNet: More Features From Cheap Operations | Kai Han, Yunhe Wang, Qi Tian, Jianyuan Guo, Chunjing Xu, Chang Xu | Deploying convolutional neural networks (CNNs) on embedded devices is difficult due to the limited memory and computation resources. The redundancy in feature maps is an important characteristic of those successful CNNs, but has rarely been investigated in neural architecture design. This paper proposes a novel Ghost module to generate more feature maps from cheap operations. Based on a set of intrinsic feature maps, we apply a series of linear transformations with cheap cost to generate many ghost feature maps that could fully reveal information underlying intrinsic features. The proposed Ghost module can be taken as a plug-and-play component to upgrade existing convolutional neural networks. Ghost bottlenecks are designed to stack Ghost modules, and then the lightweight GhostNet can be easily established. Experiments conducted on benchmarks demonstrate that the proposed Ghost module is an impressive alternative of convolution layers in baseline models, and our GhostNet can achieve higher recognition performance (e.g. 75.7% top-1 accuracy) than MobileNetV3 with similar computational cost on the ImageNet ILSVRC-2012 classification dataset. Code is available at https://github.com/huawei-noah/ghostnet. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Han_GhostNet_More_Features_From_Cheap_Operations_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.11907 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Han_GhostNet_More_Features_From_Cheap_Operations_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Han_GhostNet_More_Features_From_Cheap_Operations_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization | Yue Jiang, Dantong Ji, Zhizhong Han, Matthias Zwicker | We propose SDFDiff, a novel approach for image-based shape optimization using differentiable rendering of 3D shapes represented by signed distance functions (SDFs). Compared to other representations, SDFs have the advantage that they can represent shapes with arbitrary topology, and that they guarantee watertight surfaces. We apply our approach to the problem of multi-view 3D reconstruction, where we achieve high reconstruction quality and can capture complex topology of 3D objects. In addition, we employ a multi-resolution strategy to obtain a robust optimization algorithm. We further demonstrate that our SDF-based differentiable renderer can be integrated with deep learning models, which opens up options for learning approaches on 3D objects without 3D supervision. In particular, we apply our method to single-view 3D reconstruction and achieve state-of-the-art results. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jiang_SDFDiff_Differentiable_Rendering_of_Signed_Distance_Fields_for_3D_Shape_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.07109 | https://www.youtube.com/watch?v=T7STQSQb_So | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_SDFDiff_Differentiable_Rendering_of_Signed_Distance_Fields_for_3D_Shape_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_SDFDiff_Differentiable_Rendering_of_Signed_Distance_Fields_for_3D_Shape_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Jiang_SDFDiff_Differentiable_Rendering_CVPR_2020_supplemental.zip | null | null |
Self2Self With Dropout: Learning Self-Supervised Denoising From Single Image | Yuhui Quan, Mingqin Chen, Tongyao Pang, Hui Ji | In last few years, supervised deep learning has emerged as one powerful tool for image denoising, which trains a denoising network over an external dataset of noisy/clean image pairs. However, the requirement on a high-quality training dataset limits the broad applicability of the denoising networks. Recently, there have been a few works that allow training a denoising network on the set of external noisy images only. Taking one step further, this paper proposes a self-supervised learning method which only uses the input noisy image itself for training. In the proposed method, the network is trained with dropout on the pairs of Bernoulli-sampled instances of the input image, and the result is estimated by averaging the predictions generated from multiple instances of the trained model with dropout. The experiments show that the proposed method not only significantly outperforms existing single-image learning or non-learning methods, but also is competitive to the denoising networks trained on external datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Quan_Self2Self_With_Dropout_Learning_Self-Supervised_Denoising_From_Single_Image_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=EzvaNiXrNAw | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Quan_Self2Self_With_Dropout_Learning_Self-Supervised_Denoising_From_Single_Image_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Quan_Self2Self_With_Dropout_Learning_Self-Supervised_Denoising_From_Single_Image_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Quan_Self2Self_With_Dropout_CVPR_2020_supplemental.pdf | null | null |
A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image | Yuyu Guo, Lei Bi, Euijoon Ahn, Dagan Feng, Qian Wang, Jinman Kim | Dynamic medical images are often limited in its application due to the large radiation doses and longer image scanning and reconstruction times. Existing methods attempt to reduce the volume samples in the dynamic sequence by interpolating the volumes between the acquired samples. However, these methods are limited to either 2D images and/or are unable to support large but periodic variations in the functional motion between the image volume samples. In this paper, we present a spatiotemporal volumetric interpolation network (SVIN) designed for 4D dynamic medical images. SVIN introduces dual networks: the first is the spatiotemporal motion network that leverages the 3D convolutional neural network (CNN) for unsupervised parametric volumetric registration to derive spatiotemporal motion field from a pair of image volumes; the second is the sequential volumetric interpolation network, which uses the derived motion field to interpolate image volumes, together with a new regression-based module to characterize the periodic motion cycles in functional organ structures. We also introduce an adaptive multi-scale architecture to capture the volumetric large anatomy motions. Experimental results demonstrated that our SVIN outperformed state-of-the-art temporal medical interpolation methods and natural video interpolation method that has been extended to support volumetric images. Code is available at [1]. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Guo_A_Spatiotemporal_Volumetric_Interpolation_Network_for_4D_Dynamic_Medical_Image_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.12680 | https://www.youtube.com/watch?v=CMcxisYox4U | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_A_Spatiotemporal_Volumetric_Interpolation_Network_for_4D_Dynamic_Medical_Image_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_A_Spatiotemporal_Volumetric_Interpolation_Network_for_4D_Dynamic_Medical_Image_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Where Am I Looking At? Joint Location and Orientation Estimation by Cross-View Matching | Yujiao Shi, Xin Yu, Dylan Campbell, Hongdong Li | Cross-view geo-localization is the problem of estimating the position and orientation (latitude, longitude and azimuth angle) of a camera at ground level given a large-scale database of geo-tagged aerial (eg., satellite) images. Existing approaches treat the task as a pure location estimation problem by learning discriminative feature descriptors, but neglect orientation alignment. It is well-recognized that knowing the orientation between ground and aerial images can significantly reduce matching ambiguity between these two views, especially when the ground-level images have a limited Field of View (FoV) instead of a full field-of-view panorama. Therefore, we design a Dynamic Similarity Matching network to estimate cross-view orientation alignment during localization. In particular, we address the cross-view domain gap by applying a polar transform to the aerial images to approximately align the images up to an unknown azimuth angle. Then, a two-stream convolutional network is used to learn deep features from the ground and polar-transformed aerial images. Finally, we obtain the orientation by computing the correlation between cross-view features, which also provides a more accurate measure of feature similarity, improving location recall. Experiments on standard datasets demonstrate that our method significantly improves state-of-the-art performance. Remarkably, we improve the top-1 location recall rate on the CVUSA dataset by a factor of 1.5x for panoramas with known orientation, by a factor of 3.3x for panoramas with unknown orientation, and by a factor of 6x for 180-degree FoV images with unknown orientation. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shi_Where_Am_I_Looking_At_Joint_Location_and_Orientation_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.03860 | https://www.youtube.com/watch?v=m1XIkhS1I54 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Shi_Where_Am_I_Looking_At_Joint_Location_and_Orientation_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Shi_Where_Am_I_Looking_At_Joint_Location_and_Orientation_Estimation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Shi_Where_Am_I_CVPR_2020_supplemental.pdf | null | null |
Towards Large Yet Imperceptible Adversarial Image Perturbations With Perceptual Color Distance | Zhengyu Zhao, Zhuoran Liu, Martha Larson | The success of image perturbations that are designed to fool image classifier is assessed in terms of both adversarial effect and visual imperceptibility. The conventional assumption on imperceptibility is that perturbations should strive for tight Lp-norm bounds in RGB space. In this work, we drop this assumption by pursuing an approach that exploits human color perception, and more specifically, minimizing perturbation size with respect to perceptual color distance. Our first approach, Perceptual Color distance C&W (PerC-C&W), extends the widely-used C&W approach and produces larger RGB perturbations. PerC-C&W is able to maintain adversarial strength, while contributing to imperceptibility. Our second approach, Perceptual Color distance Alternating Loss (PerC-AL), achieves the same outcome, but does so more efficiently by alternating between the classification loss and perceptual color difference when updating perturbations. Experimental evaluation shows PerC approaches outperform conventional Lp approaches in terms of robustness and transferability, and also demonstrates that the PerC distance can provide added value on top of existing structure-based methods to creating image perturbations. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhao_Towards_Large_Yet_Imperceptible_Adversarial_Image_Perturbations_With_Perceptual_Color_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.02466 | https://www.youtube.com/watch?v=2j74B_9VaJ8 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Towards_Large_Yet_Imperceptible_Adversarial_Image_Perturbations_With_Perceptual_Color_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Towards_Large_Yet_Imperceptible_Adversarial_Image_Perturbations_With_Perceptual_Color_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhao_Towards_Large_Yet_CVPR_2020_supplemental.pdf | null | null |
Assessing Image Quality Issues for Real-World Problems | Tai-Yin Chiu, Yinan Zhao, Danna Gurari | We introduce a new large-scale dataset that links the assessment of image quality issues to two practical vision tasks: image captioning and visual question answering. First, we identify for 39,181 images taken by people who are blind whether each is sufficient quality to recognize the content as well as what quality flaws are observed from six options. These labels serve as a critical foundation for us to make the following contributions: (1) a new problem and algorithms for deciding whether an image is insufficient quality to recognize the content and so not captionable, (2) a new problem and algorithms for deciding which of six quality flaws an image contains, (3) a new problem and algorithms for deciding whether a visual question is unanswerable due to unrecognizable content versus the content of interest being missing from the field of view, and (4) a novel application of more efficiently creating a large-scale image captioning dataset by automatically deciding whether an image is insufficient quality and so should not be captioned. We publicly-share our datasets and code to facilitate future extensions of this work: https://vizwiz.org. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chiu_Assessing_Image_Quality_Issues_for_Real-World_Problems_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12511 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chiu_Assessing_Image_Quality_Issues_for_Real-World_Problems_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chiu_Assessing_Image_Quality_Issues_for_Real-World_Problems_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chiu_Assessing_Image_Quality_CVPR_2020_supplemental.pdf | null | null |
Adaptive Dilated Network With Self-Correction Supervision for Counting | Shuai Bai, Zhiqun He, Yu Qiao, Hanzhe Hu, Wei Wu, Junjie Yan | The counting problem aims to estimate the number of objects in images. Due to large scale variation and labeling deviations, it remains a challenging task. The static density map supervised learning framework is widely used in existing methods, which uses the Gaussian kernel to generate a density map as the learning target and utilizes the Euclidean distance to optimize the model. However, the framework is intolerable to the labeling deviations and can not reflect the scale variation. In this paper, we propose an adaptive dilated convolution and a novel supervised learning framework named self-correction (SC) supervision. In the supervision level, the SC supervision utilizes the outputs of the model to iteratively correct the annotations and employs the SC loss to simultaneously optimize the model from both the whole and the individuals. In the feature level, the proposed adaptive dilated convolution predicts a continuous value as the specific dilation rate for each location, which adapts the scale variation better than a discrete and static dilation rate. Extensive experiments illustrate that our approach has achieved a consistent improvement on four challenging benchmarks. Especially, our approach achieves better performance than the state-of-the-art methods on all benchmark datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Bai_Adaptive_Dilated_Network_With_Self-Correction_Supervision_for_Counting_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Bai_Adaptive_Dilated_Network_With_Self-Correction_Supervision_for_Counting_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Bai_Adaptive_Dilated_Network_With_Self-Correction_Supervision_for_Counting_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Bai_Adaptive_Dilated_Network_CVPR_2020_supplemental.pdf | null | null |
Camouflaged Object Detection | Deng-Ping Fan, Ge-Peng Ji, Guolei Sun, Ming-Ming Cheng, Jianbing Shen, Ling Shao | We present a comprehensive study on a new task named camouflaged object detection (COD), which aims to identify objects that are "seamlessly" embedded in their surroundings. The high intrinsic similarities between the target object and the background make COD far more challenging than the traditional object detection task. To address this issue, we elaborately collect a novel dataset, called COD10K, which comprises 10,000 images covering camouflaged objects in various natural scenes, over 78 object categories. All the images are densely annotated with category, bounding-box, object-/instance-level, and matting-level labels. This dataset could serve as a catalyst for progressing many vision tasks, e.g., localization, segmentation, and alpha-matting, etc. In addition, we develop a simple but effective framework for COD, termed Search Identification Network (SINet). Without any bells and whistles, SINet outperforms various state-of-the-art object detection baselines on all datasets tested, making it a robust, general framework that can help facilitate future research in COD. Finally, we conduct a large-scale COD study, evaluating 13 cutting-edge models, providing some interesting findings, and showing several potential applications. Our research offers the community an opportunity to explore more in this new field. The code will be available at https://github.com/DengPingFan/SINet/. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Fan_Camouflaged_Object_Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Fan_Camouflaged_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Fan_Camouflaged_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Fan_Camouflaged_Object_Detection_CVPR_2020_supplemental.zip | null | null |
Why Having 10,000 Parameters in Your Camera Model Is Better Than Twelve | Thomas Schops, Viktor Larsson, Marc Pollefeys, Torsten Sattler | Camera calibration is an essential first step in setting up 3D Computer Vision systems. Commonly used parametric camera models are limited to a few degrees of freedom and thus often do not optimally fit to complex real lens distortion. In contrast, generic camera models allow for very accurate calibration due to their flexibility. Despite this, they have seen little use in practice. In this paper, we argue that this should change. We propose a calibration pipeline for generic models that is fully automated, easy to use, and can act as a drop-in replacement for parametric calibration, with a focus on accuracy. We compare our results to parametric calibrations. Considering stereo depth estimation and camera pose estimation as examples, we show that the calibration error acts as a bias on the results. We thus argue that in contrast to current common practice, generic models should be preferred over parametric ones whenever possible. To facilitate this, we released our calibration pipeline at https://github.com/puzzlepaint/camera_calibration, making both easy-to-use and accurate camera calibration available to everyone. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Schops_Why_Having_10000_Parameters_in_Your_Camera_Model_Is_Better_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Schops_Why_Having_10000_Parameters_in_Your_Camera_Model_Is_Better_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Schops_Why_Having_10000_Parameters_in_Your_Camera_Model_Is_Better_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Schops_Why_Having_10000_CVPR_2020_supplemental.pdf | null | null |
BiDet: An Efficient Binarized Object Detector | Ziwei Wang, Ziyi Wu, Jiwen Lu, Jie Zhou | In this paper, we propose a binarized neural network learning method called BiDet for efficient object detection. Conventional network binarization methods directly quantize the weights and activations in one-stage or two-stage detectors with constrained representational capacity, so that the information redundancy in the networks causes numerous false positives and degrades the performance significantly. On the contrary, our BiDet fully utilizes the representational capacity of the binary neural networks for object detection by redundancy removal, through which the detection precision is enhanced with alleviated false positives. Specifically, we generalize the information bottleneck (IB) principle to object detection, where the amount of information in the high-level feature maps is constrained and the mutual information between the feature maps and object detection is maximized. Meanwhile, we learn sparse object priors so that the posteriors are concentrated on informative detection prediction with false positive elimination. Extensive experiments on the PASCAL VOC and COCO datasets show that our method outperforms the state-of-the-art binary neural networks by a sizable margin. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_BiDet_An_Efficient_Binarized_Object_Detector_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.03961 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_BiDet_An_Efficient_Binarized_Object_Detector_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_BiDet_An_Efficient_Binarized_Object_Detector_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Searching for Actions on the Hyperbole | Teng Long, Pascal Mettes, Heng Tao Shen, Cees G. M. Snoek | In this paper, we introduce hierarchical action search. Starting from the observation that hierarchies are mostly ignored in the action literature, we retrieve not only individual actions but also relevant and related actions, given an action name or video example as input. We propose a hyperbolic action network, which is centered around a hyperbolic space shared by action hierarchies and videos. Our discriminative hyperbolic embedding projects actions on the shared space while jointly optimizing hypernym-hyponym relations between action pairs and a large margin separation between all actions. The projected actions serve as hyperbolic prototypes that we match with projected video representations. The result is a learned space where videos are positioned in entailment cones formed by different subtrees. To perform search in this space, we start from a query and increasingly enlarge its entailment cone to retrieve hierarchically relevant action videos. Experiments on three action datasets with new hierarchy annotations show the effectiveness of our approach for hierarchical action search by name and by video example, regardless of whether queried actions have been seen or not during training. Our implementation is available at https://github.com/Tenglon/hyperbolic_action | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Long_Searching_for_Actions_on_the_Hyperbole_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Long_Searching_for_Actions_on_the_Hyperbole_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Long_Searching_for_Actions_on_the_Hyperbole_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Long_Searching_for_Actions_CVPR_2020_supplemental.pdf | null | null |
SG-NN: Sparse Generative Neural Networks for Self-Supervised Scene Completion of RGB-D Scans | Angela Dai, Christian Diller, Matthias Niessner | We present a novel approach that converts partial and noisy RGB-D scans into high-quality 3D scene reconstructions by inferring unobserved scene geometry. Our approach is fully self-supervised and can hence be trained solely on incomplete, real-world scans. To achieve, self-supervision, we remove frames from a given (incomplete) 3D scan in order to make it even more incomplete; self-supervision is then formulated by correlating the two levels of partialness of the same scan while masking out regions that have never been observed. Through generalization across a large training set, we can then predict 3D scene completions even without seeing any 3D scan of entirely complete geometry. Combined with a new 3D sparse generative convolutional neural network architecture, our method is able to predict highly detailed surfaces in a coarse-to-fine hierarchical fashion that outperform existing state-of-the-art methods by a significant margin in terms of reconstruction quality. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Dai_SG-NN_Sparse_Generative_Neural_Networks_for_Self-Supervised_Scene_Completion_of_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=gADedihdK8c | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Dai_SG-NN_Sparse_Generative_Neural_Networks_for_Self-Supervised_Scene_Completion_of_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Dai_SG-NN_Sparse_Generative_Neural_Networks_for_Self-Supervised_Scene_Completion_of_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Dai_SG-NN_Sparse_Generative_CVPR_2020_supplemental.pdf | null | null |
Stereoscopic Flash and No-Flash Photography for Shape and Albedo Recovery | Xu Cao, Michael Waechter, Boxin Shi, Ye Gao, Bo Zheng, Yasuyuki Matsushita | We present a minimal imaging setup that harnesses both geometric and photometric approaches for shape and albedo recovery. We adopt a stereo camera and a flashlight to capture a stereo image pair and a flash/no-flash pair. From the stereo image pair, we recover a rough shape that captures low-frequency shape variation without high-frequency details. From the flash/no-flash pair, we derive an image formation model for Lambertian objects under natural lighting, based on which a fine normal map is obtained and fused with the rough shape. Further, we use the flash/no-flash pair for cast shadow detection and albedo canceling, making the shape recovery robust against shadows and albedo variation. We verify the effectiveness of our approach on both synthetic and real-world data. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cao_Stereoscopic_Flash_and_No-Flash_Photography_for_Shape_and_Albedo_Recovery_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cao_Stereoscopic_Flash_and_No-Flash_Photography_for_Shape_and_Albedo_Recovery_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cao_Stereoscopic_Flash_and_No-Flash_Photography_for_Shape_and_Albedo_Recovery_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Cao_Stereoscopic_Flash_and_CVPR_2020_supplemental.pdf | null | null |
What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation | Jiahua Dong, Yang Cong, Gan Sun, Bineng Zhong, Xiaowei Xu | Unsupervised domain adaptation has attracted growing research attention on semantic segmentation. However, 1) most existing models cannot be directly applied into lesions transfer of medical images, due to the diverse appearances of same lesion among different datasets; 2) equal attention has been paid into all semantic representations instead of neglecting irrelevant knowledge, which leads to negative transfer of untransferable knowledge. To address these challenges, we develop a new unsupervised semantic transfer model including two complementary modules (i.e., T_D and T_F ) for endoscopic lesions segmentation, which can alternatively determine where and how to explore transferable domain-invariant knowledge between labeled source lesions dataset (e.g., gastroscope) and unlabeled target diseases dataset (e.g., enteroscopy). Specifically, T_D focuses on where to translate transferable visual information of medical lesions via residual transferability-aware bottleneck, while neglecting untransferable visual characterizations. Furthermore, T_F highlights how to augment transferable semantic features of various lesions and automatically ignore untransferable representations, which explores domain-invariant knowledge and in return improves the performance of T_D. To the end, theoretical analysis and extensive experiments on medical endoscopic dataset and several non-medical public datasets well demonstrate the superiority of our proposed model. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Dong_What_Can_Be_Transferred_Unsupervised_Domain_Adaptation_for_Endoscopic_Lesions_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.11500 | https://www.youtube.com/watch?v=DDV8X_z6Aac | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Dong_What_Can_Be_Transferred_Unsupervised_Domain_Adaptation_for_Endoscopic_Lesions_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Dong_What_Can_Be_Transferred_Unsupervised_Domain_Adaptation_for_Endoscopic_Lesions_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning to Generate 3D Training Data Through Hybrid Gradient | Dawei Yang, Jia Deng | Synthetic images rendered by graphics engines are a promising source for training deep networks. However, it is challenging to ensure that they can help train a network to perform well on real images, because a graphics-based generation pipeline requires numerous design decisions such as the selection of 3D shapes and the placement of the camera. In this work, we propose a new method that optimizes the generation of 3D training data based on what we call "hybrid gradient". We parametrize the design decisions as a real vector, and combine the approximate gradient and the analytical gradient to obtain the hybrid gradient of the network performance with respect to this vector. We evaluate our approach on the task of estimating surface normal, depth or intrinsic decomposition from a single image. Experiments on standard benchmarks show that our approach can outperform the prior state of the art on optimizing the generation of 3D training data, particularly in terms of computational efficiency. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_Learning_to_Generate_3D_Training_Data_Through_Hybrid_Gradient_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Learning_to_Generate_3D_Training_Data_Through_Hybrid_Gradient_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Learning_to_Generate_3D_Training_Data_Through_Hybrid_Gradient_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yang_Learning_to_Generate_CVPR_2020_supplemental.pdf | null | null |
On Joint Estimation of Pose, Geometry and svBRDF From a Handheld Scanner | Carolin Schmitt, Simon Donne, Gernot Riegler, Vladlen Koltun, Andreas Geiger | We propose a novel formulation for joint recovery of camera pose, object geometry and spatially-varying BRDF. The input to our approach is a sequence of RGB-D images captured by a mobile, hand-held scanner that actively illuminates the scene with point light sources. Compared to previous works that jointly estimate geometry and materials from a hand-held scanner, we formulate this problem using a single objective function that can be minimized using off-the-shelf gradient-based solvers. By integrating material clustering as a differentiable operation into the optimization process, we avoid pre-processing heuristics and demonstrate that our model is able to determine the correct number of specular materials independently. We provide a study on the importance of each component in our formulation and on the requirements of the initial geometry. We show that optimizing over the poses is crucial for accurately recovering fine details and show that our approach naturally results in a semantically meaningful material segmentation. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Schmitt_On_Joint_Estimation_of_Pose_Geometry_and_svBRDF_From_a_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Schmitt_On_Joint_Estimation_of_Pose_Geometry_and_svBRDF_From_a_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Schmitt_On_Joint_Estimation_of_Pose_Geometry_and_svBRDF_From_a_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Synchronizing Probability Measures on Rotations via Optimal Transport | Tolga Birdal, Michael Arbel, Umut Simsekli, Leonidas J. Guibas | We introduce a new paradigm, `measure synchronization', for synchronizing graphs with measure-valued edges. We formulate this problem as maximization of the cycle-consistency in the space of probability measures over relative rotations. In particular, we aim at estimating marginal distributions of absolute orientations by synchronizing the `conditional' ones, which are defined on the Riemannian manifold of quaternions. Such graph optimization on distributions-on-manifolds enables a natural treatment of multimodal hypotheses, ambiguities and uncertainties arising in many computer vision applications such as SLAM, SfM, and object pose estimation. We first formally define the problem as a generalization of the classical rotation graph synchronization, where in our case the vertices denote probability measures over rotations. We then measure the quality of the synchronization by using Sinkhorn divergences, which reduces to other popular metrics such as Wasserstein distance or the maximum mean discrepancy as limit cases. We propose a nonparametric Riemannian particle optimization approach to solve the problem. Even though the problem is non-convex, by drawing a connection to the recently proposed sparse optimization methods, we show that the proposed algorithm converges to the global optimum in a special case of the problem under certain conditions. Our qualitative and quantitative experiments show the validity of our approach and we bring in new perspectives to the study of synchronization. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Birdal_Synchronizing_Probability_Measures_on_Rotations_via_Optimal_Transport_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00663 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Birdal_Synchronizing_Probability_Measures_on_Rotations_via_Optimal_Transport_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Birdal_Synchronizing_Probability_Measures_on_Rotations_via_Optimal_Transport_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Birdal_Synchronizing_Probability_Measures_CVPR_2020_supplemental.pdf | null | null |
Camera Trace Erasing | Chang Chen, Zhiwei Xiong, Xiaoming Liu, Feng Wu | Camera trace is a unique noise produced in digital imaging process. Most existing forensic methods analyze camera trace to identify image origins. In this paper, we address a new low-level vision problem, camera trace erasing, to reveal the weakness of trace-based forensic methods. A comprehensive investigation on existing anti-forensic methods reveals that it is non-trivial to effectively erase camera trace while avoiding the destruction of content signal. To reconcile these two demands, we propose Siamese Trace Erasing (SiamTE), in which a novel hybrid loss is designed on the basis of Siamese architecture for network training. Specifically, we propose embedded similarity, truncated fidelity, and cross identity to form the hybrid loss. Compared with existing anti-forensic methods, SiamTE has a clear advantage for camera trace erasing, which is demonstrated in three representative tasks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Camera_Trace_Erasing_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.06951 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Camera_Trace_Erasing_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Camera_Trace_Erasing_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_Camera_Trace_Erasing_CVPR_2020_supplemental.pdf | null | null |
Robust 3D Self-Portraits in Seconds | Zhe Li, Tao Yu, Chuanyu Pan, Zerong Zheng, Yebin Liu | In this paper, we propose an efficient method for robust 3D self-portraits using a single RGBD camera. Benefiting from the proposed PIFusion and lightweight bundle adjustment algorithm, our method can generate detailed 3D self-portraits in seconds and shows the ability to handle subjects wearing extremely loose clothes. To achieve highly efficient and robust reconstruction, we propose PIFusion, which combines learning-based 3D recovery with volumetric non-rigid fusion to generate accurate sparse partial scans of the subject. Moreover, a non-rigid volumetric deformation method is proposed to continuously refine the learned shape prior. Finally, a lightweight bundle adjustment algorithm is proposed to guarantee that all the partial scans can not only "loop" with each other but also remain consistent with the selected live key observations. The results and experiments show that the proposed method achieves more robust and efficient 3D self-portraits compared with state-of-the-art methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Robust_3D_Self-Portraits_in_Seconds_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.02460 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Robust_3D_Self-Portraits_in_Seconds_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Robust_3D_Self-Portraits_in_Seconds_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Instance Shadow Detection | Tianyu Wang, Xiaowei Hu, Qiong Wang, Pheng-Ann Heng, Chi-Wing Fu | Instance shadow detection is a brand new problem, aiming to find shadow instances paired with object instances. To approach it, we first prepare a new dataset called SOBA, named after Shadow-OBject Association, with 3,623 pairs of shadow and object instances in 1,000 photos, each with individual labeled masks. Second, we design LISA, named after Light-guided Instance Shadow-object Association, an end-to-end framework to automatically predict the shadow and object instances, together with the shadow-object associations and light direction. Then, we pair up the predicted shadow and object instances, and match them with the predicted shadow-object associations to generate the final results. In our evaluations, we formulate a new metric named the shadow-object average precision to measure the performance of our results. Further, we conducted various experiments and demonstrate our method's applicability on light direction estimation and photo editing. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Instance_Shadow_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.07034 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Instance_Shadow_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Instance_Shadow_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
MemNAS: Memory-Efficient Neural Architecture Search With Grow-Trim Learning | Peiye Liu, Bo Wu, Huadong Ma, Mingoo Seok | Recent studies on automatic neural architecture search techniques have demonstrated significant performance, competitive to or even better than hand-crafted neural architectures. However, most of the existing search approaches tend to use residual structures and a concatenation connection between shallow and deep features. A resulted neural network model, therefore, is non-trivial for resource-constraint devices to execute since such a model requires large memory to store network parameters and intermediate feature maps along with excessive computing complexity. To address this challenge, we propose MemNAS, a novel growing and trimming based neural architecture search framework that optimizes not only performance but also memory requirement of an inference network. Specifically, in the search process, we consider running memory use, including network parameters and the essential intermediate feature maps memory requirement, as an optimization objective along with performance. Besides, to improve the accuracy of the search, we extract the correlation information among multiple candidate architectures to rank them and then choose the candidates with desired performance and memory efficiency. On the ImageNet classification task, our MemNAS achieves 75.4% accuracy, 0.7% higher than MobileNetV2 with 42.1% less memory requirement. Additional experiments confirm that the proposed MemNAS can perform well across the different targets of the trade-off between accuracy and memory consumption. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_MemNAS_Memory-Efficient_Neural_Architecture_Search_With_Grow-Trim_Learning_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=YmE6cWK9rpk | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_MemNAS_Memory-Efficient_Neural_Architecture_Search_With_Grow-Trim_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_MemNAS_Memory-Efficient_Neural_Architecture_Search_With_Grow-Trim_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Deep Distance Transform for Tubular Structure Segmentation in CT Scans | Yan Wang, Xu Wei, Fengze Liu, Jieneng Chen, Yuyin Zhou, Wei Shen, Elliot K. Fishman, Alan L. Yuille | Tubular structure segmentation in medical images, e.g., segmenting vessels in CT scans, serves as a vital step in the use of computers to aid in screening early stages of related diseases. But automatic tubular structure segmentation in CT scans is a challenging problem, due to issues such as poor contrast, noise and complicated background. A tubular structure usually has a cylinder-like shape which can be well represented by its skeleton and cross-sectional radii (scales). Inspired by this, we propose a geometry-aware tubular structure segmentation method, Deep Distance Transform (DDT), which combines intuitions from the classical distance transform for skeletonization and modern deep segmentation networks. DDT first learns a multi-task network to predict a segmentation mask for a tubular structure and a distance map. Each value in the map represents the distance from each tubular structure voxel to the tubular structure surface. Then the segmentation mask is refined by leveraging the shape prior reconstructed from the distance map. We apply our DDT on six medical image datasets. Results show that (1) DDT can boost tubular structure segmentation performance significantly (e.g., over 13% DSC improvement for pancreatic duct segmentation), and (2) DDT additionally provides a geometrical measurement for a tubular structure, which is important for clinical diagnosis (e.g., the cross-sectional scale of a pancreatic duct can be an indicator for pancreatic cancer). | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Deep_Distance_Transform_for_Tubular_Structure_Segmentation_in_CT_Scans_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.03383 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Deep_Distance_Transform_for_Tubular_Structure_Segmentation_in_CT_Scans_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Deep_Distance_Transform_for_Tubular_Structure_Segmentation_in_CT_Scans_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_Deep_Distance_Transform_CVPR_2020_supplemental.pdf | null | null |
FineGym: A Hierarchical Video Dataset for Fine-Grained Action Understanding | Dian Shao, Yue Zhao, Bo Dai, Dahua Lin | On public benchmarks, current action recognition techniques have achieved great success. However, when used in real-world applications, e.g. sport analysis, which requires the capability of parsing an activity into phases and differentiating between subtly different actions, their performances remain far from being satisfactory. To take action recognition to a new level, we develop FineGym, a new dataset built on top of gymnasium videos. Compared to existing action recognition datasets, FineGym is distinguished in richness, quality, and diversity. In particular, it provides temporal annotations at both action and sub-action levels with a three-level semantic hierarchy. For example, a "balance beam" activity will be annotated as a sequence of elementary sub-actions derived from five sets: "leap-jump-hop", "beam-turns", "flight-salto", "flight-handspring", and "dismount", where the sub-action in each set will be further annotated with finely defined class labels. This new level of granularity presents significant challenges for action recognition, e.g. how to parse the temporal structures from a coherent action, and how to distinguish between subtly different action classes. We systematically investigates different methods on this dataset and obtains a number of interesting findings. We hope this dataset could advance research towards action understanding. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shao_FineGym_A_Hierarchical_Video_Dataset_for_Fine-Grained_Action_Understanding_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.06704 | https://www.youtube.com/watch?v=ChvW59jM4O0 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Shao_FineGym_A_Hierarchical_Video_Dataset_for_Fine-Grained_Action_Understanding_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Shao_FineGym_A_Hierarchical_Video_Dataset_for_Fine-Grained_Action_Understanding_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Shao_FineGym_A_Hierarchical_CVPR_2020_supplemental.zip | null | null |
What Does Plate Glass Reveal About Camera Calibration? | Qian Zheng, Jinnan Chen, Zhan Lu, Boxin Shi, Xudong Jiang, Kim-Hui Yap, Ling-Yu Duan, Alex C. Kot | This paper aims to calibrate the orientation of glass and the field of view of the camera from a single reflection-contaminated image. We show how a reflective amplitude coefficient map can be used as a calibration cue. Different from existing methods, the proposed solution is free from image contents. To reduce the impact of a noisy calibration cue estimated from a reflection-contaminated image, we propose two strategies: an optimization-based method that imposes part of though reliable entries on the map and a learning-based method that fully exploits all entries. We collect a dataset containing 320 samples as well as their camera parameters for evaluation. We demonstrate that our method not only facilitates a general single image camera calibration method that leverages image contents but also contributes to improving the performance of single image reflection removal. Furthermore, we show our byproduct output helps alleviate the ill-posed problem of estimating the panorama from a single image. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zheng_What_Does_Plate_Glass_Reveal_About_Camera_Calibration_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_What_Does_Plate_Glass_Reveal_About_Camera_Calibration_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_What_Does_Plate_Glass_Reveal_About_Camera_Calibration_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
One Man's Trash Is Another Man's Treasure: Resisting Adversarial Examples by Adversarial Examples | Chang Xiao, Changxi Zheng | Modern image classification systems are often built on deep neural networks, which suffer from adversarial examples--images with deliberately crafted, imperceptible noise to mislead the network's classification. To defend against adversarial examples, a plausible idea is to obfuscate the network's gradient with respect to the input image. This general idea has inspired a long line of defense methods. Yet, almost all of them have proven vulnerable. We revisit this seemingly flawed idea from a radically different perspective. We embrace the omnipresence of adversarial examples and the numerical procedure of crafting them, and turn this harmful attacking process into a useful defense mechanism. Our defense method is conceptually simple: before feeding an input image for classification, transform it by finding an adversarial example on a pre-trained external model. We evaluate our method against a wide range of possible attacks. On both CIFAR-10 and Tiny ImageNet datasets, our method is significantly more robust than state-of-the-art methods. Particularly, in comparison to adversarial training, our method offers lower training cost as well as stronger robustness. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xiao_One_Mans_Trash_Is_Another_Mans_Treasure_Resisting_Adversarial_Examples_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=4Gpvnpt8oRA | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xiao_One_Mans_Trash_Is_Another_Mans_Treasure_Resisting_Adversarial_Examples_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xiao_One_Mans_Trash_Is_Another_Mans_Treasure_Resisting_Adversarial_Examples_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Xiao_One_Mans_Trash_CVPR_2020_supplemental.pdf | null | null |
Image Processing Using Multi-Code GAN Prior | Jinjin Gu, Yujun Shen, Bolei Zhou | Despite the success of Generative Adversarial Networks (GANs) in image synthesis, applying trained GAN models to real image processing remains challenging. Previous methods typically invert a target image back to the latent space either by back-propagation or by learning an additional encoder. However, the reconstructions from both of the methods are far from ideal. In this work, we propose a novel approach, called mGANprior, to incorporate the well-trained GANs as effective prior to a variety of image processing tasks. In particular, we employ multiple latent codes to generate multiple feature maps at some intermediate layer of the generator, then compose them with adaptive channel importance to recover the input image. Such an over-parameterization of the latent space significantly improves the image reconstruction quality, outperforming existing competitors. The resulting high-fidelity image reconstruction enables the trained GAN models as prior to many real-world applications, such as image colorization, super-resolution, image inpainting, and semantic manipulation. We further analyze the properties of the layer-wise representation learned by GAN models and shed light on what knowledge each layer is capable of representing. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gu_Image_Processing_Using_Multi-Code_GAN_Prior_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.07116 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gu_Image_Processing_Using_Multi-Code_GAN_Prior_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gu_Image_Processing_Using_Multi-Code_GAN_Prior_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
ColorFool: Semantic Adversarial Colorization | Ali Shahin Shamsabadi, Ricardo Sanchez-Matilla, Andrea Cavallaro | Adversarial attacks that generate small Lp norm perturbations to mislead classifiers have limited success in black-box settings and with unseen classifiers. These attacks are also not robust to defenses that use denoising filters and to adversarial training procedures. Instead, adversarial attacks that generate unrestricted perturbations are more robust to defenses, are generally more successful in black-box settings and are more transferable to unseen classifiers. However, unrestricted perturbations may be noticeable to humans. In this paper, we propose a content-based black-box adversarial attack that generates unrestricted perturbations by exploiting image semantics to selectively modify colors within chosen ranges that are perceived as natural by humans. We show that the proposed approach, ColorFool, outperforms in terms of success rate, robustness to defense frameworks and transferability, five state-of-the-art adversarial attacks on two different tasks, scene and object classification, when attacking three state-of-the-art deep neural networks using three standard datasets. The source code is available at https://github.com/smartcameras/ColorFool. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shamsabadi_ColorFool_Semantic_Adversarial_Colorization_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Shamsabadi_ColorFool_Semantic_Adversarial_Colorization_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Shamsabadi_ColorFool_Semantic_Adversarial_Colorization_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Shamsabadi_ColorFool_Semantic_Adversarial_CVPR_2020_supplemental.zip | null | null |
Bi3D: Stereo Depth Estimation via Binary Classifications | Abhishek Badki, Alejandro Troccoli, Kihwan Kim, Jan Kautz, Pradeep Sen, Orazio Gallo | Stereo-based depth estimation is a cornerstone of computer vision, with state-of-the-art methods delivering accurate results in real time. For several applications such as autonomous navigation, however, it may be useful to trade accuracy for lower latency. We present Bi3D, a method that estimates depth via a series of binary classifications. Rather than testing if objects are at a particular depth D, as existing stereo methods do, it classifies them as being closer or farther than D. This property offers a powerful mechanism to balance accuracy and latency. Given a strict time budget, Bi3D can detect objects closer than a given distance in as little as a few milliseconds, or estimate depth with arbitrarily coarse quantization, with complexity linear with the number of quantization levels. Bi3D can also use the allotted quantization levels to get continuous depth, but in a specific depth range. For standard stereo (i.e., continuous depth on the whole range), our method is close to or on par with state-of-the-art, finely tuned stereo methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Badki_Bi3D_Stereo_Depth_Estimation_via_Binary_Classifications_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.07274 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Badki_Bi3D_Stereo_Depth_Estimation_via_Binary_Classifications_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Badki_Bi3D_Stereo_Depth_Estimation_via_Binary_Classifications_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Badki_Bi3D_Stereo_Depth_CVPR_2020_supplemental.zip | null | null |
D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry | Nan Yang, Lukas von Stumberg, Rui Wang, Daniel Cremers | We propose D3VO as a novel framework for monocular visual odometry that exploits deep networks on three levels -- deep depth, pose and uncertainty estimation. We first propose a novel self-supervised monocular depth estimation network trained on stereo videos without any external supervision. In particular, it aligns the training image pairs into similar lighting condition with predictive brightness transformation parameters. Besides, we model the photometric uncertainties of pixels on the input images, which improves the depth estimation accuracy and provides a learned weighting function for the photometric residuals in direct (feature-less) visual odometry. Evaluation results show that the proposed network outperforms state-of-the-art self-supervised depth estimation networks. D3VO tightly incorporates the predicted depth, pose and uncertainty into a direct visual odometry method to boost both the front-end tracking as well as the back-end non-linear optimization. We evaluate D3VO in terms of monocular visual odometry on both the KITTI odometry benchmark and the EuRoC MAV dataset. The results show that D3VO outperforms state-of-the-art traditional monocular VO methods by a large margin. It also achieves comparable results to state-of-the-art stereo/LiDAR odometry on KITTI and to the state-of-the-art visual-inertial odometry on EuRoC MAV, while using only a single camera. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_D3VO_Deep_Depth_Deep_Pose_and_Deep_Uncertainty_for_Monocular_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.01060 | https://www.youtube.com/watch?v=bS9u28-2p7w | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_D3VO_Deep_Depth_Deep_Pose_and_Deep_Uncertainty_for_Monocular_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_D3VO_Deep_Depth_Deep_Pose_and_Deep_Uncertainty_for_Monocular_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yang_D3VO_Deep_Depth_CVPR_2020_supplemental.pdf | null | null |
Fantastic Answers and Where to Find Them: Immersive Question-Directed Visual Attention | Ming Jiang, Shi Chen, Jinhui Yang, Qi Zhao | While most visual attention studies focus on bottom-up attention with restricted field-of-view, real-life situations are filled with embodied vision tasks. The role of attention is more significant in the latter due to the information overload, and attention to the most important regions is critical to the success of tasks. The effects of visual attention on task performance in this context have also been widely ignored. This research addresses a number of challenges to bridge this research gap, on both the data and model aspects. Specifically, we introduce the first dataset of top-down attention in immersive scenes. The Immersive Question-directed Visual Attention (IQVA) dataset features visual attention and corresponding task performance (i.e., answer correctness). It consists of 975 questions and answers collected from people viewing 360deg videos in a head-mounted display. Analyses of the data demonstrate a significant correlation between people's task performance and their eye movements, suggesting the role of attention in task performance. With that, a neural network is developed to encode the differences of correct and incorrect attention and jointly predict the two. The proposed attention model for the first time takes into account answer correctness, whose outputs naturally distinguish important regions from distractions. This study with new data and features may enable new tasks that leverage attention and answer correctness, and inspire new research that reveals the process behind decision making in performing various tasks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jiang_Fantastic_Answers_and_Where_to_Find_Them_Immersive_Question-Directed_Visual_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=N2-7j7uS0qo | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_Fantastic_Answers_and_Where_to_Find_Them_Immersive_Question-Directed_Visual_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_Fantastic_Answers_and_Where_to_Find_Them_Immersive_Question-Directed_Visual_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Jiang_Fantastic_Answers_and_CVPR_2020_supplemental.pdf | null | null |
Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction | Maosen Li, Siheng Chen, Yangheng Zhao, Ya Zhang, Yanfeng Wang, Qi Tian | We propose novel dynamic multiscale graph neural networks (DMGNN) to predict 3D skeleton-based human motions. The core idea of DMGNN is to use a multiscale graph to comprehensively model the internal relations of a human body for motion feature learning. This multiscale graph is adaptive during training and dynamic across network layers. Based on this graph, we propose a multiscale graph computational unit (MGCU) to extract features at individual scales and fuse features across scales. The entire model is action-category-agnostic and follows an encoder-decoder framework. The encoder consists of a sequence of MGCUs to learn motion features. The decoder uses a proposed graph-based gate recurrent unit to generate future poses. Extensive experiments show that the proposed DMGNN outperforms state-of-the-art methods in both short and long-term predictions on the datasets of Human 3.6M and CMU Mocap. We further investigate the learned multiscale graphs for the interpretability. The codes could be downloaded from https://github.com/limaosen0/DMGNN. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Dynamic_Multiscale_Graph_Neural_Networks_for_3D_Skeleton_Based_Human_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.08802 | https://www.youtube.com/watch?v=nNWA-EOBwWw | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Dynamic_Multiscale_Graph_Neural_Networks_for_3D_Skeleton_Based_Human_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Dynamic_Multiscale_Graph_Neural_Networks_for_3D_Skeleton_Based_Human_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Dynamic_Multiscale_Graph_CVPR_2020_supplemental.zip | null | null |
Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes From a Single Image | Yinyu Nie, Xiaoguang Han, Shihui Guo, Yujian Zheng, Jian Chang, Jian Jun Zhang | Semantic reconstruction of indoor scenes refers to both scene understanding and object reconstruction. Existing works either address one part of this problem or focus on independent objects. In this paper, we bridge the gap between understanding and reconstruction, and propose an end-to-end solution to jointly reconstruct room layout, object bounding boxes and meshes from a single image. Instead of separately resolving scene understanding and object reconstruction, our method builds upon a holistic scene context and proposes a coarse-to-fine hierarchy with three components: 1. room layout with camera pose; 2. 3D object bounding boxes; 3. object meshes. We argue that understanding the context of each component can assist the task of parsing the others, which enables joint understanding and reconstruction. The experiments on the SUN RGB-D and Pix3D datasets demonstrate that our method consistently outperforms existing methods in indoor layout estimation, 3D object detection and mesh reconstruction. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Nie_Total3DUnderstanding_Joint_Layout_Object_Pose_and_Mesh_Reconstruction_for_Indoor_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.12212 | https://www.youtube.com/watch?v=jUIGpWFybJs | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Nie_Total3DUnderstanding_Joint_Layout_Object_Pose_and_Mesh_Reconstruction_for_Indoor_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Nie_Total3DUnderstanding_Joint_Layout_Object_Pose_and_Mesh_Reconstruction_for_Indoor_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Nie_Total3DUnderstanding_Joint_Layout_CVPR_2020_supplemental.pdf | null | null |
GPS-Net: Graph Property Sensing Network for Scene Graph Generation | Xin Lin, Changxing Ding, Jinquan Zeng, Dacheng Tao | Scene graph generation (SGG) aims to detect objects in an image along with their pairwise relationships. There are three key properties of scene graph that have been underexplored in recent works: namely, the edge direction information, the difference in priority between nodes, and the long-tailed distribution of relationships. Accordingly, in this paper, we propose a Graph Property Sensing Network (GPS-Net) that fully explores these three properties for SGG. First, we propose a novel message passing module that augments the node feature with node-specific contextual information and encodes the edge direction information via a tri-linear model. Second, we introduce a node priority sensitive loss to reflect the difference in priority between nodes during training. This is achieved by designing a mapping function that adjusts the focusing parameter in the focal loss. Third, since the frequency of relationships is affected by the long-tailed distribution problem, we mitigate this issue by first softening the distribution and then enabling it to be adjusted for each subject-object pair according to their visual appearance. Systematic experiments demonstrate the effectiveness of the proposed techniques. Moreover, GPS-Net achieves state-of-the-art performance on three popular databases: VG, OI, and VRD by significant gains under various settings and metrics. The code and models are available at https://github.com/taksau/GPS-Net. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lin_GPS-Net_Graph_Property_Sensing_Network_for_Scene_Graph_Generation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_GPS-Net_Graph_Property_Sensing_Network_for_Scene_Graph_Generation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_GPS-Net_Graph_Property_Sensing_Network_for_Scene_Graph_Generation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lin_GPS-Net_Graph_Property_CVPR_2020_supplemental.pdf | null | null |
Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes | Zhengqin Li, Yu-Ying Yeh, Manmohan Chandraker | Recovering the 3D shape of transparent objects using a small number of unconstrained natural images is an ill-posed problem. Complex light paths induced by refraction and reflection have prevented both traditional and deep multiview stereo from solving this challenge. We propose a physically-based network to recover 3D shape of transparent objects using a few images acquired with a mobile phone camera, under a known but arbitrary environment map. Our novel contributions include a normal representation that enables the network to model complex light transport through local computation, a rendering layer that models refractions and reflections, a cost volume specifically designed for normal refinement of transparent shapes and a feature mapping based on predicted normals for 3D point cloud reconstruction. We render a synthetic dataset to encourage the model to learn refractive light transport across different views. Our experiments show successful recovery of high-quality 3D geometry for complex transparent shapes using as few as 5-12 natural images. Code and data will be publicly released.Recovering the 3D shape of transparent objects using a small number of unconstrained natural images is an ill-posed problem. Complex light paths induced by refraction and reflection have prevented both traditional and deep multiview stereo from solving this challenge. We propose a physically-based network to recover 3D shape of transparent objects using a few images acquired with a mobile phone camera, under a known but arbitrary environment map. Our novel contributions include a normal representation that enables the network to model complex light transport through local computation, a rendering layer that models refractions and reflections, a cost volume specifically designed for normal refinement of transparent shapes and a feature mapping based on predicted normals for 3D point cloud reconstruction. We render a synthetic dataset to encourage the model to learn refractive light transport across different views. Our experiments show successful recovery of high-quality 3D geometry for complex transparent shapes using as few as 5-12 natural images. Code and data will be publicly released. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Through_the_Looking_Glass_Neural_3D_Reconstruction_of_Transparent_Shapes_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.10904 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Through_the_Looking_Glass_Neural_3D_Reconstruction_of_Transparent_Shapes_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Through_the_Looking_Glass_Neural_3D_Reconstruction_of_Transparent_Shapes_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Through_the_Looking_CVPR_2020_supplemental.zip | null | null |
Recursive Social Behavior Graph for Trajectory Prediction | Jianhua Sun, Qinhong Jiang, Cewu Lu | Social interaction is an important topic in human trajectory prediction to generate plausible paths. In this paper, we present a novel insight of group-based social interaction model to explore relationships among pedestrians. We recursively extract social representations supervised by group-based annotations and formulate them into a social behavior graph, called Recursive Social Behavior Graph. Our recursive mechanism explores the representation power largely. Graph Convolutional Neural Network then is used to propagate social interaction information in such a graph. With the guidance of Recursive Social Behavior Graph, we surpass state-of-the-art methods on ETH and UCY dataset for 11.1% in ADE and 10.8% in FDE in average, and successfully predict complex social behaviors. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Sun_Recursive_Social_Behavior_Graph_for_Trajectory_Prediction_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.10402 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Sun_Recursive_Social_Behavior_Graph_for_Trajectory_Prediction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Sun_Recursive_Social_Behavior_Graph_for_Trajectory_Prediction_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Attention Scaling for Crowd Counting | Xiaoheng Jiang, Li Zhang, Mingliang Xu, Tianzhu Zhang, Pei Lv, Bing Zhou, Xin Yang, Yanwei Pang | Convolutional Neural Network (CNN) based methods generally take crowd counting as a regression task by outputting crowd densities. They learn the mapping between image contents and crowd density distributions. Though having achieved promising results, these data-driven counting networks are prone to overestimate or underestimate people counts of regions with different density patterns, which degrades the whole count accuracy. To overcome this problem, we propose an approach to alleviate the counting performance differences in different regions. Specifically, our approach consists of two networks named Density Attention Network (DANet) and Attention Scaling Network (ASNet). DANet provides ASNet with attention masks related to regions of different density levels. ASNet first generates density maps and scaling factors and then multiplies them by attention masks to output separate attention-based density maps. These density maps are summed to give the final density map. The attention scaling factors help attenuate the estimation errors in different regions. Furthermore, we present a novel Adaptive Pyramid Loss (APLoss) to hierarchically calculate the estimation losses of sub-regions, which alleviates the training bias. Extensive experiments on four challenging datasets (ShanghaiTech Part A, UCF_CC_50, UCF-QNRF, and WorldExpo'10) demonstrate the superiority of the proposed approach. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jiang_Attention_Scaling_for_Crowd_Counting_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_Attention_Scaling_for_Crowd_Counting_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_Attention_Scaling_for_Crowd_Counting_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
FocalMix: Semi-Supervised Learning for 3D Medical Image Detection | Dong Wang, Yuan Zhang, Kexin Zhang, Liwei Wang | Applying artificial intelligence techniques in medical imaging is one of the most promising areas in medicine. However, most of the recent success in this area highly relies on large amounts of carefully annotated data, whereas annotating medical images is a costly process. In this paper, we propose a novel method, called FocalMix, which, to the best of our knowledge, is the first to leverage recent advances in semi-supervised learning (SSL) for 3D medical image detection. We conducted extensive experiments on two widely used datasets for lung nodule detection, LUNA16 and NLST. Results show that our proposed SSL methods can achieve a substantial improvement of up to 17.3% over state-of-the-art supervised learning approaches with 400 unlabeled CT scans. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_FocalMix_Semi-Supervised_Learning_for_3D_Medical_Image_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.09108 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_FocalMix_Semi-Supervised_Learning_for_3D_Medical_Image_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_FocalMix_Semi-Supervised_Learning_for_3D_Medical_Image_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Bi-Directional Relationship Inferring Network for Referring Image Segmentation | Zhiwei Hu, Guang Feng, Jiayu Sun, Lihe Zhang, Huchuan Lu | Most existing methods do not explicitly formulate the mutual guidance between vision and language. In this work, we propose a bi-directional relationship inferring network (BRINet) to model the dependencies of cross-modal information. In detail, the vision-guided linguistic attention is used to learn the adaptive linguistic context corresponding to each visual region. Combining with the language-guided visual attention, a bi-directional cross-modal attention module (BCAM) is built to learn the relationship between multi-modal features. Thus, the ultimate semantic context of the target object and referring expression can be represented accurately and consistently. Moreover, a gated bi-directional fusion module (GBFM) is designed to integrate the multi-level features where a gate function is used to guide the bi-directional flow of multi-level information. Extensive experiments on four benchmark datasets demonstrate that the proposed method outperforms other state-of-the-art methods under different evaluation metrics. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hu_Bi-Directional_Relationship_Inferring_Network_for_Referring_Image_Segmentation_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=0wh5XXdKUBI | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hu_Bi-Directional_Relationship_Inferring_Network_for_Referring_Image_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hu_Bi-Directional_Relationship_Inferring_Network_for_Referring_Image_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation | Matias Tassano, Julie Delon, Thomas Veit | In this paper, we propose a state-of-the-art video denoising algorithm based on a convolutional neural network architecture. Until recently, video denoising with neural networks had been a largely under explored domain, and existing methods could not compete with the performance of the best patch-based methods. The approach we introduce in this paper, called FastDVDnet, shows similar or better performance than other state-of-the-art competitors with significantly lower computing times. In contrast to other existing neural network denoisers, our algorithm exhibits several desirable properties such as fast runtimes, and the ability to handle a wide range of noise levels with a single network model. The characteristics of its architecture make it possible to avoid using a costly motion compensation stage while achieving excellent performance. The combination between its denoising performance and lower computational load makes this algorithm attractive for practical denoising applications. We compare our method with different state-of-art algorithms, both visually and with respect to objective quality metrics. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tassano_FastDVDnet_Towards_Real-Time_Deep_Video_Denoising_Without_Flow_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1907.01361 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tassano_FastDVDnet_Towards_Real-Time_Deep_Video_Denoising_Without_Flow_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tassano_FastDVDnet_Towards_Real-Time_Deep_Video_Denoising_Without_Flow_Estimation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Tassano_FastDVDnet_Towards_Real-Time_CVPR_2020_supplemental.pdf | null | null |
Composed Query Image Retrieval Using Locally Bounded Features | Mehrdad Hosseinzadeh, Yang Wang | Composed query image retrieval is a new problem where the query consists of an image together with a requested modification expressed via a textual sentence. The goal is then to retrieve the images that are generally similar to the query image, but differ according to the requested modification. Previous methods usually consider the image as a whole. In this paper, we propose a novel method that represents the image using a set of local areas in the image. The relationship between each word in the modification text and each area in the image is then explicitly established, allowing the model to accurately correlate the modification text to parts of the image. We conduct extensive experiments on three benchmark datasets. The results show that our method outperforms other state-of-the-art approaches by a considerable margin. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hosseinzadeh_Composed_Query_Image_Retrieval_Using_Locally_Bounded_Features_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hosseinzadeh_Composed_Query_Image_Retrieval_Using_Locally_Bounded_Features_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hosseinzadeh_Composed_Query_Image_Retrieval_Using_Locally_Bounded_Features_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Variational-EM-Based Deep Learning for Noise-Blind Image Deblurring | Yuesong Nan, Yuhui Quan, Hui Ji | Non-blind deblurring is an important problem encountered in many image restoration tasks. The focus of non-blind deblurring is on how to suppress noise magnification during deblurring. In practice, it often happens that the noise level of input image is unknown and varies among different images. This paper aims at developing a deep learning framework for deblurring images with unknown noise level. Based on the framework of variational expectation maximization (EM), an iterative noise-blind deblurring scheme is proposed which integrates the estimation of noise level and the quantification of image prior uncertainty. Then, the proposed scheme is unrolled to a neural network (NN) where image prior is modeled by NN with uncertainty quantification. Extensive experiments showed that the proposed method not only outperformed existing noise-blind deblurring methods by a large margin, but also outperformed those state-of-the-art image deblurring methods designed/trained with known noise level. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Nan_Variational-EM-Based_Deep_Learning_for_Noise-Blind_Image_Deblurring_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Nan_Variational-EM-Based_Deep_Learning_for_Noise-Blind_Image_Deblurring_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Nan_Variational-EM-Based_Deep_Learning_for_Noise-Blind_Image_Deblurring_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Nan_Variational-EM-Based_Deep_Learning_CVPR_2020_supplemental.pdf | null | null |
Central Similarity Quantization for Efficient Image and Video Retrieval | Li Yuan, Tao Wang, Xiaopeng Zhang, Francis EH Tay, Zequn Jie, Wei Liu, Jiashi Feng | Existing data-dependent hashing methods usually learn hash functions from pairwise or triplet data relationships, which only capture the data similarity locally, and often suffer from low learning efficiency and low collision rate. In this work, we propose a new global similarity metric, termed as central similarity, with which the hash codes of similar data pairs are encouraged to approach a common center and those for dissimilar pairs to converge to different centers, to improve hash learning efficiency and retrieval accuracy. We principally formulate the computation of the proposed central similarity metric by introducing a new concept, i.e., hash center that refers to a set of data points scattered in the Hamming space with a sufficient mutual distance between each other. We then provide an efficient method to construct well separated hash centers by leveraging the Hadamard matrix and Bernoulli distributions. Finally, we propose the Central Similarity Quantization (CSQ) that optimizes the central similarity between data points w.r.t. their hash centers instead of optimizing the local similarity. CSQ is generic and applicable to both image and video hashing scenarios. Extensive experiments on large-scale image and video retrieval tasks demonstrate that CSQ can generate cohesive hash codes for similar data pairs and dispersed hash codes for dissimilar pairs, achieving a noticeable boost in retrieval performance, i.e. 3%-20% in mAP over the previous state-of-the-arts. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yuan_Central_Similarity_Quantization_for_Efficient_Image_and_Video_Retrieval_CVPR_2020_paper.pdf | http://arxiv.org/abs/1908.00347 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yuan_Central_Similarity_Quantization_for_Efficient_Image_and_Video_Retrieval_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yuan_Central_Similarity_Quantization_for_Efficient_Image_and_Video_Retrieval_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yuan_Central_Similarity_Quantization_CVPR_2020_supplemental.pdf | null | null |
Taking a Deeper Look at Co-Salient Object Detection | Deng-Ping Fan, Zheng Lin, Ge-Peng Ji, Dingwen Zhang, Huazhu Fu, Ming-Ming Cheng | Co-salient object detection (CoSOD) is a newly emerging and rapidly growing branch of salient object detection (SOD), which aims to detect the co-occurring salient objects in multiple images. However, existing CoSOD datasets often have a serious data bias, which assumes that each group of images contains salient objects of similar visual appearances. This bias results in the ideal settings and the effectiveness of the models, trained on existing datasets, may be impaired in real-life situations, where the similarity is usually semantic or conceptual. To tackle this issue, we first collect a new high-quality dataset, named CoSOD3k, which contains 3,316 images divided into 160 groups with multiple level annotations, i.e., category, bounding box, object, and instance levels. CoSOD3k makes a significant leap in terms of diversity, difficulty and scalability, benefiting related vision tasks. Besides, we comprehensively summarize 34 cutting-edge algorithms, benchmarking 19 of them over four existing CoSOD datasets (MSRC, iCoSeg, Image Pair and CoSal2015) and our CoSOD3k with a total of 61K images (largest scale), and reporting group-level performance analysis. Finally, we discuss the challenge and future work of CoSOD. Our study would give a strong boost to growth in the CoSOD community. Benchmark toolbox and results are available on our project page. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Fan_Taking_a_Deeper_Look_at_Co-Salient_Object_Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Fan_Taking_a_Deeper_Look_at_Co-Salient_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Fan_Taking_a_Deeper_Look_at_Co-Salient_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Fan_Taking_a_Deeper_CVPR_2020_supplemental.zip | null | null |
Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics | Yuezun Li, Xin Yang, Pu Sun, Honggang Qi, Siwei Lyu | AI-synthesized face-swapping videos, commonly known as DeepFakes, is an emerging problem threatening the trustworthiness of online information. The need to develop and evaluate DeepFake detection algorithms calls for datasets of DeepFake videos. However, current DeepFake datasets suffer from low visual quality and do not resemble DeepFake videos circulated on the Internet. We present a new large-scale challenging DeepFake video dataset, Celeb-DF, which contains 5,639 high-quality DeepFake videos of celebrities generated using improved synthesis process. We conduct a comprehensive evaluation of DeepFake detection methods and datasets to demonstrate the escalated level of challenges posed by Celeb-DF. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Celeb-DF_A_Large-Scale_Challenging_Dataset_for_DeepFake_Forensics_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Celeb-DF_A_Large-Scale_Challenging_Dataset_for_DeepFake_Forensics_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Celeb-DF_A_Large-Scale_Challenging_Dataset_for_DeepFake_Forensics_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
TEA: Temporal Excitation and Aggregation for Action Recognition | Yan Li, Bin Ji, Xintian Shi, Jianguo Zhang, Bin Kang, Limin Wang | Temporal modeling is key for action recognition in videos. It normally considers both short-range motions and long-range aggregations. In this paper, we propose a Temporal Excitation and Aggregation (TEA) block, including a motion excitation (ME) module and a multiple temporal aggregation (MTA) module, specifically designed to capture both short- and long-range temporal evolution. In particular, for short-range motion modeling, the ME module calculates the feature-level temporal differences from spatiotemporal features. It then utilizes the differences to excite the motion-sensitive channels of the features. The long-range temporal aggregations in previous works are typically achieved by stacking a large number of local temporal convolutions. Each convolution processes a local temporal window at a time. In contrast, the MTA module proposes to deform the local convolution to a group of sub-convolutions, forming a hierarchical residual architecture. Without introducing additional parameters, the features will be processed with a series of sub-convolutions, and each frame could complete multiple temporal aggregations with neighborhoods. The final equivalent receptive field of temporal dimension is accordingly enlarged, which is capable of modeling the long-range temporal relationship over distant frames. The two components of the TEA block are complementary in temporal modeling. Finally, our approach achieves impressive results at low FLOPs on several action recognition benchmarks, such as Kinetics, Something-Something, HMDB51, and UCF101, which confirms its effectiveness and efficiency. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_TEA_Temporal_Excitation_and_Aggregation_for_Action_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.01398 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_TEA_Temporal_Excitation_and_Aggregation_for_Action_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_TEA_Temporal_Excitation_and_Aggregation_for_Action_Recognition_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_TEA_Temporal_Excitation_CVPR_2020_supplemental.pdf | null | null |
Unsupervised Person Re-Identification via Softened Similarity Learning | Yutian Lin, Lingxi Xie, Yu Wu, Chenggang Yan, Qi Tian | Person re-identification (re-ID) is an important topic in computer vision. This paper studies the unsupervised setting of re-ID, which does not require any labeled information and thus is freely deployed to new scenarios. There are very few studies under this setting, and one of the best approach till now used iterative clustering and classification, so that unlabeled images are clustered into pseudo classes for a classifier to get trained, and the updated features are used for clustering and so on. This approach suffers two problems, namely, the difficulty of determining the number of clusters, and the hard quantization loss in clustering. In this paper, we follow the iterative training mechanism but discard clustering, since it incurs loss from hard quantization, yet its only product, image-level similarity, can be easily replaced by pairwise computation and a softened classification task. With these improvements, our approach becomes more elegant and is more robust to hyper-parameter changes. Experiments on two image-based and video-based datasets demonstrate state-of-the-art performance under the unsupervised re-ID setting. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lin_Unsupervised_Person_Re-Identification_via_Softened_Similarity_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.03547 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Unsupervised_Person_Re-Identification_via_Softened_Similarity_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Unsupervised_Person_Re-Identification_via_Softened_Similarity_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Frequency Domain Compact 3D Convolutional Neural Networks | Hanting Chen, Yunhe Wang, Han Shu, Yehui Tang, Chunjing Xu, Boxin Shi, Chao Xu, Qi Tian, Chang Xu | This paper studies the compression and acceleration of 3-dimensional convolutional neural networks (3D CNNs). To reduce the memory cost and computational complexity of deep neural networks, a number of algorithms have been explored by discovering redundant parameters in pre-trained networks. However, most of existing methods are designed for processing neural networks consisting of 2-dimensional convolution filters (i.e. image classification and detection) and cannot be straightforwardly applied for 3-dimensional filters (i.e. time series data). In this paper, we develop a novel approach for eliminating redundancy in the time dimensionality of 3D convolution filters by converting them into the frequency domain through a series of learned optimal transforms with extremely fewer parameters. Moreover, these transforms are forced to be orthogonal, and the calculation of feature maps can be accomplished in the frequency domain to achieve considerable speed-up rates. Experimental results on benchmark 3D CNN models and datasets demonstrate that the proposed Frequency Domain Compact 3D CNNs (FDC3D) can achieve the state-of-the-art performance, e.g. a 2x speed-up ratio on the 3D-ResNet-18 without obviously affecting its accuracy. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Frequency_Domain_Compact_3D_Convolutional_Neural_Networks_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Frequency_Domain_Compact_3D_Convolutional_Neural_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Frequency_Domain_Compact_3D_Convolutional_Neural_Networks_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Revisiting Saliency Metrics: Farthest-Neighbor Area Under Curve | Sen Jia, Neil D. B. Bruce | In this paper, we propose a new metric to address the long-standing problem of center bias in saliency evaluation. We first show that distribution-based metrics cannot measure saliency performance across datasets due to ambiguity in the choice of standard deviation, especially for Convolutional Neural Networks. Therefore, our proposed metric is AUC-based because ROC curves are relatively robust to the standard deviation problem. However, this requires sufficient unique values in the saliency prediction to compute AUC scores. Secondly, we propose a global smoothing function for the problem of few value degrees in predicted saliency output. Compared with random noise, our smoothing function can create unique values without losing the existing relative saliency relationship. Finally, we show our proposed AUC-based metric can generate a more directional negative set for evaluation, denoted as Farthest-Neighbor AUC (FN-AUC). Our experiments show FN-AUC can measure spatial biases, central and peripheral, more effectively than S-AUC without penalizing the fixation locations. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jia_Revisiting_Saliency_Metrics_Farthest-Neighbor_Area_Under_Curve_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.10540 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jia_Revisiting_Saliency_Metrics_Farthest-Neighbor_Area_Under_Curve_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jia_Revisiting_Saliency_Metrics_Farthest-Neighbor_Area_Under_Curve_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Jia_Revisiting_Saliency_Metrics_CVPR_2020_supplemental.pdf | null | null |
Structured Compression by Weight Encryption for Unstructured Pruning and Quantization | Se Jung Kwon, Dongsoo Lee, Byeongwook Kim, Parichay Kapoor, Baeseong Park, Gu-Yeon Wei | Model compression techniques, such as pruning and quantization, are becoming increasingly important to reduce the memory footprints and the amount of computations. Despite model size reduction, achieving performance enhancement on devices is, however, still challenging mainly due to the irregular representations of sparse matrix formats. This paper proposes a new weight representation scheme for Sparse Quantized Neural Networks, specifically achieved by fine-grained and unstructured pruning method. The representation is encrypted in a structured regular format, which can be efficiently decoded through XOR-gate network during inference in a parallel manner. We demonstrate various deep learning models that can be compressed and represented by our proposed format with fixed and high compression ratio. For example, for fully-connected layers of AlexNet on ImageNet dataset, we can represent the sparse weights by only 0.28 bits/weight for 1-bit quantization and 91% pruning rate with a fixed decoding rate and full memory bandwidth usage. Decoding through XOR-gate network can be performed without any model accuracy degradation with additional patch data associated with small overhead. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kwon_Structured_Compression_by_Weight_Encryption_for_Unstructured_Pruning_and_Quantization_CVPR_2020_paper.pdf | http://arxiv.org/abs/1905.10138 | https://www.youtube.com/watch?v=MOsCX_xV474 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kwon_Structured_Compression_by_Weight_Encryption_for_Unstructured_Pruning_and_Quantization_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kwon_Structured_Compression_by_Weight_Encryption_for_Unstructured_Pruning_and_Quantization_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
End of preview. Expand
in Data Studio
CVPR 2020 Accepted Paper Meta Info Dataset
This dataset is collect from the CVPR 2020 Open Access website (https://openaccess.thecvf.com/CVPR2020) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/cvpr2020). For researchers who are interested in doing analysis of CVPR 2020 accepted papers and potential trends, you can use the already cleaned up json files. Each row contains the meta information of a paper in the CVPR 2020 conference. To explore more AI & Robotic papers (NIPS/ICML/ICLR/IROS/ICRA/etc) and AI equations, feel free to navigate the Equation Search Engine (http://www.deepnlp.org/search/equation) as well as the AI Agent Search Engine to find the deployed AI Apps and Agents (http://www.deepnlp.org/search/agent) in your domain.
Equations Latex code and Papers Search Engine
Meta Information of Json File of Paper
{
"title": "Dual Super-Resolution Learning for Semantic Segmentation",
"authors": "Li Wang, Dong Li, Yousong Zhu, Lu Tian, Yi Shan",
"abstract": "Current state-of-the-art semantic segmentation methods often apply high-resolution input to attain high performance, which brings large computation budgets and limits their applications on resource-constrained devices. In this paper, we propose a simple and flexible two-stream framework named Dual Super-Resolution Learning (DSRL) to effectively improve the segmentation accuracy without introducing extra computation costs. Specifically, the proposed method consists of three parts: Semantic Segmentation Super-Resolution (SSSR), Single Image Super-Resolution (SISR) and Feature Affinity (FA) module, which can keep high-resolution representations with low-resolution input while simultaneously reducing the model computation complexity. Moreover, it can be easily generalized to other tasks, e.g., human pose estimation. This simple yet effective method leads to strong representations and is evidenced by promising performance on both semantic segmentation and human pose estimation. Specifically, for semantic segmentation on CityScapes, we can achieve \\geq2% higher mIoU with similar FLOPs, and keep the performance with 70% FLOPs. For human pose estimation, we can gain \\geq2% mAP with the same FLOPs and maintain mAP with 30% fewer FLOPs. Code and models are available at https://github.com/wanglixilinx/DSRL.",
"pdf": "https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Dual_Super-Resolution_Learning_for_Semantic_Segmentation_CVPR_2020_paper.pdf",
"bibtex": "https://openaccess.thecvf.com",
"url": "https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Dual_Super-Resolution_Learning_for_Semantic_Segmentation_CVPR_2020_paper.html",
"detail_url": "https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Dual_Super-Resolution_Learning_for_Semantic_Segmentation_CVPR_2020_paper.html",
"tags": "CVPR 2020"
}
Related
AI Agent Marketplace and Search
AI Agent Marketplace and Search
Robot Search
Equation and Academic search
AI & Robot Comprehensive Search
AI & Robot Question
AI & Robot Community
AI Agent Marketplace Blog
AI Agent Reviews
AI Agent Marketplace Directory
Microsoft AI Agents Reviews
Claude AI Agents Reviews
OpenAI AI Agents Reviews
Saleforce AI Agents Reviews
AI Agent Builder Reviews
AI Equation
List of AI Equations and Latex
List of Math Equations and Latex
List of Physics Equations and Latex
List of Statistics Equations and Latex
List of Machine Learning Equations and Latex
- Downloads last month
- 15