2019 IEEE/CVF International Conference on Computer Vision (ICCV)最新文献_第10页

Progressive Fusion Video Super-Resolution Network via Exploiting Non-Local Spatio-Temporal Correlations 利用非局部时空相关性的渐进式融合视频超分辨率网络

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00320

Peng Yi, Zhongyuan Wang, Kui Jiang, Junjun Jiang, Jiayi Ma

引用次数: 176

Action Assessment by Joint Relation Graphs 联合关系图的行为评价

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00643

Jiahui Pan, Jibin Gao, Weishi Zheng

引用次数: 68

Quasi-Globally Optimal and Efficient Vanishing Point Estimation in Manhattan World 曼哈顿世界的准全局最优有效消失点估计

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00173

Haoang Li, Ji Zhao, J. Bazin, Wen Chen, Zhe Liu, Yunhui Liu

{"title":"Quasi-Globally Optimal and Efficient Vanishing Point Estimation in Manhattan World","authors":"Haoang Li, Ji Zhao, J. Bazin, Wen Chen, Zhe Liu, Yunhui Liu","doi":"10.1109/ICCV.2019.00173","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00173","url":null,"abstract":"The image lines projected from parallel 3D lines intersect at a common point called the vanishing point (VP). Manhattan world holds for the scenes with three orthogonal VPs. In Manhattan world, given several lines in a calibrated image, we aim at clustering them by three unknown-but-sought VPs. The VP estimation can be reformulated as computing the rotation between the Manhattan frame and the camera frame. To compute this rotation, state-of-the-art methods are based on either data sampling or parameter search, and they fail to guarantee the accuracy and efficiency simultaneously. In contrast, we propose to hybridize these two strategies. We first compute two degrees of freedom (DOF) of the above rotation by two sampled image lines, and then search for the optimal third DOF based on the branch-and-bound. Our sampling accelerates our search by reducing the search space and simplifying the bound computation. Our search is not sensitive to noise and achieves quasi-global optimality in terms of maximizing the number of inliers. Experiments on synthetic and real-world images showed that our method outperforms state-of-the-art approaches in terms of accuracy and/or efficiency.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"17 1","pages":"1646-1654"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81163091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 25

Wavelet Domain Style Transfer for an Effective Perception-Distortion Tradeoff in Single Image Super-Resolution 小波域风格转移在单幅超分辨率图像中有效的感知-失真权衡

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00317

Xin Deng, Ren Yang, Mai Xu, P. Dragotti

{"title":"Wavelet Domain Style Transfer for an Effective Perception-Distortion Tradeoff in Single Image Super-Resolution","authors":"Xin Deng, Ren Yang, Mai Xu, P. Dragotti","doi":"10.1109/ICCV.2019.00317","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00317","url":null,"abstract":"In single image super-resolution (SISR), given a low-resolution (LR) image, one wishes to ﬁnd a high-resolution (HR) version of it which is both accurate and photorealistic. Recently, it has been shown that there exists a fundamental tradeoff between low distortion and high perceptual quality, and the generative adversarial network (GAN) is demonstrated to approach the perception-distortion (PD) bound effectively. In this paper, we propose a novel method based on wavelet domain style transfer (WDST), which achieves a better PD tradeoff than the GAN based methods. Speciﬁcally, we propose to use 2D stationary wavelet transform (SWT) to decompose one image into low-frequency and high-frequency sub-bands. For the low-frequency sub-band, we improve its objective quality through an enhancement network. For the high-frequency sub-band, we propose to use WDST to effectively improve its perceptual quality. By feat of the perfect reconstruction property of wavelets, these sub-bands can be re-combined to obtain an image which has simultaneously high objective and perceptual quality. The numerical results on various datasets show that our method achieves the best trade-off between the distortion and perceptual quality among the existing state-of-the-art SISR methods.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"40 1","pages":"3076-3085"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81548857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 59

Controllable Attention for Structured Layered Video Decomposition 结构化分层视频分解的可控注意力

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00583

Jean-Baptiste Alayrac, João Carreira, Relja Arandjelovi'c, Andrew Zisserman

引用次数: 8

Mixture-Kernel Graph Attention Network for Situation Recognition 态势识别的混合核图注意网络

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.01046

M. Suhail, L. Sigal

引用次数: 22

C-MIDN: Coupled Multiple Instance Detection Network With Segmentation Guidance for Weakly Supervised Object Detection 弱监督目标检测的分割制导耦合多实例检测网络

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00993

Gao Yan, B. Liu, Nan Guo, Xiaochun Ye, Fang Wan, Haihang You, Dongrui Fan

{"title":"C-MIDN: Coupled Multiple Instance Detection Network With Segmentation Guidance for Weakly Supervised Object Detection","authors":"Gao Yan, B. Liu, Nan Guo, Xiaochun Ye, Fang Wan, Haihang You, Dongrui Fan","doi":"10.1109/ICCV.2019.00993","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00993","url":null,"abstract":"Weakly supervised object detection (WSOD) that only needs image-level annotations has obtained much attention recently. By combining convolutional neural network with multiple instance learning method, Multiple Instance Detection Network (MIDN) has become the most popular method to address the WSOD problem and been adopted as the initial model in many works. We argue that MIDN inclines to converge to the most discriminative object parts, which limits the performance of methods based on it. In this paper, we propose a novel Coupled Multiple Instance Detection Network (C-MIDN) to address this problem. Specifically, we use a pair of MIDNs, which work in a complementary manner with proposal removal. The localization information of the MIDNs is further coupled to obtain tighter bounding boxes and localize multiple objects. We also introduce a Segmentation Guided Proposal Removal (SGPR) algorithm to guarantee the MIL constraint after the removal and ensure the robustness of C-MIDN. Through a simple implementation of the C-MIDN with online detector refinement, we obtain 53.6% and 50.3% mAP on the challenging PASCAL VOC 2007 and 2012 benchmarks respectively, which significantly outperform the previous state-of-the-arts.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"35 1","pages":"9833-9842"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82717774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 96

Learning Meshes for Dense Visual SLAM 学习密集视觉SLAM的网格

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00595

Michael Bloesch, Tristan Laidlow, R. Clark, Stefan Leutenegger, A. Davison

{"title":"Learning Meshes for Dense Visual SLAM","authors":"Michael Bloesch, Tristan Laidlow, R. Clark, Stefan Leutenegger, A. Davison","doi":"10.1109/ICCV.2019.00595","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00595","url":null,"abstract":"Estimating motion and surrounding geometry of a moving camera remains a challenging inference problem. From an information theoretic point of view, estimates should get better as more information is included, such as is done in dense SLAM, but this is strongly dependent on the validity of the underlying models. In the present paper, we use triangular meshes as both compact and dense geometry representation. To allow for simple and fast usage, we propose a view-based formulation for which we predict the in-plane vertex coordinates directly from images and then employ the remaining vertex depth components as free variables. Flexible and continuous integration of information is achieved through the use of a residual based inference technique. This so-called factor graph encodes all information as mapping from free variables to residuals, the squared sum of which is minimised during inference. We propose the use of different types of learnable residuals, which are trained end-to-end to increase their suitability as information bearing models and to enable accurate and reliable estimation. Detailed evaluation of all components is provided on both synthetic and real data which confirms the practicability of the presented approach.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"43 1","pages":"5854-5863"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82729966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Localization of Deep Inpainting Using High-Pass Fully Convolutional Network 基于高通全卷积网络的深度喷漆定位

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00839

Haodong Li, Jiwu Huang

{"title":"Localization of Deep Inpainting Using High-Pass Fully Convolutional Network","authors":"Haodong Li, Jiwu Huang","doi":"10.1109/ICCV.2019.00839","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00839","url":null,"abstract":"Image inpainting has been substantially improved with deep learning in the past years. Deep inpainting can fill image regions with plausible contents, which are not visually apparent. Although inpainting is originally designed to repair images, it can even be used for malicious manipulations, e.g., removal of specific objects. Therefore, it is necessary to identify the presence of inpainting in an image. This paper presents a method to locate the regions manipulated by deep inpainting. The proposed method employs a fully convolutional network that is based on high-pass filtered image residuals. Firstly, we analyze and observe that the inpainted regions are more distinguishable from the untouched ones in the residual domain. Hence, a high-pass pre-filtering module is designed to get image residuals for enhancing inpainting traces. Then, a feature extraction module, which learns discriminative features from image residuals, is built with four concatenated ResNet blocks. The learned feature maps are finally enlarged by an up-sampling module, so that a pixel-wise inpainting localization map is obtained. The whole network is trained end-to-end with a loss addressing the class imbalance. Extensive experimental results evaluated on both synthetic and realistic images subjected to deep inpainting have shown the effectiveness of the proposed method.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"2003 1","pages":"8300-8309"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82892753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 58

Convex Shape Prior for Multi-Object Segmentation Using a Single Level Set Function 基于单水平集函数的凸形状先验多目标分割

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00070

Shousheng Luo, X. Tai, Limei Huo, Yang Wang, R. Glowinski

引用次数: 17