2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)最新文献_第9页

Single Image Reflection Suppression 单像反射抑制

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-07-21 DOI: 10.1109/CVPR.2017.190

Nikolaos Arvanitopoulos, R. Achanta, S. Süsstrunk

引用次数: 106

FC^4: Fully Convolutional Color Constancy with Confidence-Weighted Pooling FC^4:基于置信度加权池的全卷积颜色一致性

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-07-21 DOI: 10.1109/CVPR.2017.43

Yuanming Hu, Baoyuan Wang, Stephen Lin

{"title":"FC^4: Fully Convolutional Color Constancy with Confidence-Weighted Pooling","authors":"Yuanming Hu, Baoyuan Wang, Stephen Lin","doi":"10.1109/CVPR.2017.43","DOIUrl":"https://doi.org/10.1109/CVPR.2017.43","url":null,"abstract":"Improvements in color constancy have arisen from the use of convolutional neural networks (CNNs). However, the patch-based CNNs that exist for this problem are faced with the issue of estimation ambiguity, where a patch may contain insufficient information to establish a unique or even a limited possible range of illumination colors. Image patches with estimation ambiguity not only appear with great frequency in photographs, but also significantly degrade the quality of network training and inference. To overcome this problem, we present a fully convolutional network architecture in which patches throughout an image can carry different confidence weights according to the value they provide for color constancy estimation. These confidence weights are learned and applied within a novel pooling layer where the local estimates are merged into a global solution. With this formulation, the network is able to determine what to learn and how to pool automatically from color constancy datasets without additional supervision. The proposed network also allows for end-to-end training, and achieves higher efficiency and accuracy. On standard benchmarks, our network outperforms the previous state-of-the-art while achieving 120x greater efficiency.","PeriodicalId":6631,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"37 1","pages":"330-339"},"PeriodicalIF":0.0,"publicationDate":"2017-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73774822","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: 184

Deep Video Deblurring for Hand-Held Cameras 手持相机的深度视频去模糊

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-07-21 DOI: 10.1109/CVPR.2017.33

Shuochen Su, M. Delbracio, Jue Wang, G. Sapiro, W. Heidrich, Oliver Wang

引用次数: 445

What is and What is Not a Salient Object? Learning Salient Object Detector by Ensembling Linear Exemplar Regressors 什么是突出对象，什么不是突出对象?用集成线性样例回归学习显著目标检测器

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-07-21 DOI: 10.1109/CVPR.2017.468

Changqun Xia, Jia Li, Xiaowu Chen, Anlin Zheng, Yu Zhang

{"title":"What is and What is Not a Salient Object? Learning Salient Object Detector by Ensembling Linear Exemplar Regressors","authors":"Changqun Xia, Jia Li, Xiaowu Chen, Anlin Zheng, Yu Zhang","doi":"10.1109/CVPR.2017.468","DOIUrl":"https://doi.org/10.1109/CVPR.2017.468","url":null,"abstract":"Finding what is and what is not a salient object can be helpful in developing better features and models in salient object detection (SOD). In this paper, we investigate the images that are selected and discarded in constructing a new SOD dataset and find that many similar candidates, complex shape and low objectness are three main attributes of many non-salient objects. Moreover, objects may have diversified attributes that make them salient. As a result, we propose a novel salient object detector by ensembling linear exemplar regressors. We first select reliable foreground and background seeds using the boundary prior and then adopt locally linear embedding (LLE) to conduct manifold-preserving foregroundness propagation. In this manner, a foregroundness map can be generated to roughly pop-out salient objects and suppress non-salient ones with many similar candidates. Moreover, we extract the shape, foregroundness and attention descriptors to characterize the extracted object proposals, and a linear exemplar regressor is trained to encode how to detect salient proposals in a specific image. Finally, various linear exemplar regressors are ensembled to form a single detector that adapts to various scenarios. Extensive experimental results on 5 dataset and the new SOD dataset show that our approach outperforms 9 state-of-art methods.","PeriodicalId":6631,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"36 1","pages":"4399-4407"},"PeriodicalIF":0.0,"publicationDate":"2017-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91468917","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: 82

4D Light Field Superpixel and Segmentation 4D光场超像素和分割

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-07-21 DOI: 10.1109/CVPR.2017.710

Hao Zhu, Qi Zhang, Qing Wang

引用次数: 39

A Deep Regression Architecture with Two-Stage Re-initialization for High Performance Facial Landmark Detection 一种基于两阶段重新初始化的深度回归结构用于高性能人脸地标检测

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-07-21 DOI: 10.1109/CVPR.2017.393

Jiang-Jing Lv, Xiaohu Shao, Junliang Xing, Cheng Cheng, Xi Zhou

{"title":"A Deep Regression Architecture with Two-Stage Re-initialization for High Performance Facial Landmark Detection","authors":"Jiang-Jing Lv, Xiaohu Shao, Junliang Xing, Cheng Cheng, Xi Zhou","doi":"10.1109/CVPR.2017.393","DOIUrl":"https://doi.org/10.1109/CVPR.2017.393","url":null,"abstract":"Regression based facial landmark detection methods usually learns a series of regression functions to update the landmark positions from an initial estimation. Most of existing approaches focus on learning effective mapping functions with robust image features to improve performance. The approach to dealing with the initialization issue, however, receives relatively fewer attentions. In this paper, we present a deep regression architecture with two-stage re-initialization to explicitly deal with the initialization problem. At the global stage, given an image with a rough face detection result, the full face region is firstly re-initialized by a supervised spatial transformer network to a canonical shape state and then trained to regress a coarse landmark estimation. At the local stage, different face parts are further separately re-initialized to their own canonical shape states, followed by another regression subnetwork to get the final estimation. Our proposed deep architecture is trained from end to end and obtains promising results using different kinds of unstable initialization. It also achieves superior performances over many competing algorithms.","PeriodicalId":6631,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"1 1","pages":"3691-3700"},"PeriodicalIF":0.0,"publicationDate":"2017-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88965035","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: 224

Using Ranking-CNN for Age Estimation 使用rank - cnn进行年龄估计

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-07-21 DOI: 10.1109/CVPR.2017.86

Shixing Chen, Caojin Zhang, Ming Dong, Jialiang Le, M. Rao

引用次数: 228

Dynamic Facial Analysis: From Bayesian Filtering to Recurrent Neural Network 动态面部分析:从贝叶斯滤波到递归神经网络

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-07-21 DOI: 10.1109/CVPR.2017.167

Jinwei Gu, Xiaodong Yang, Shalini De Mello, J. Kautz

{"title":"Dynamic Facial Analysis: From Bayesian Filtering to Recurrent Neural Network","authors":"Jinwei Gu, Xiaodong Yang, Shalini De Mello, J. Kautz","doi":"10.1109/CVPR.2017.167","DOIUrl":"https://doi.org/10.1109/CVPR.2017.167","url":null,"abstract":"Facial analysis in videos, including head pose estimation and facial landmark localization, is key for many applications such as facial animation capture, human activity recognition, and human-computer interaction. In this paper, we propose to use a recurrent neural network (RNN) for joint estimation and tracking of facial features in videos. We are inspired by the fact that the computation performed in an RNN bears resemblance to Bayesian filters, which have been used for tracking in many previous methods for facial analysis from videos. Bayesian filters used in these methods, however, require complicated, problem-specific design and tuning. In contrast, our proposed RNN-based method avoids such tracker-engineering by learning from training data, similar to how a convolutional neural network (CNN) avoids feature-engineering for image classification. As an end-to-end network, the proposed RNN-based method provides a generic and holistic solution for joint estimation and tracking of various types of facial features from consecutive video frames. Extensive experimental results on head pose estimation and facial landmark localization from videos demonstrate that the proposed RNN-based method outperforms frame-wise models and Bayesian filtering. In addition, we create a large-scale synthetic dataset for head pose estimation, with which we achieve state-of-the-art performance on a benchmark dataset.","PeriodicalId":6631,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"35 1","pages":"1531-1540"},"PeriodicalIF":0.0,"publicationDate":"2017-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82885917","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: 111

Amodal Detection of 3D Objects: Inferring 3D Bounding Boxes from 2D Ones in RGB-Depth Images 三维物体的模态检测:从rgb深度图像的二维边界框推断三维边界框

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-07-21 DOI: 10.1109/CVPR.2017.50

Zhuo Deng, Longin Jan Latecki

引用次数: 99

Are Large-Scale 3D Models Really Necessary for Accurate Visual Localization? 大规模3D模型对于精确的视觉定位真的是必要的吗?

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2017-07-21 DOI: 10.1109/CVPR.2017.654

Torsten Sattler, A. Torii, Josef Sivic, M. Pollefeys, Hajime Taira, M. Okutomi, T. Pajdla

{"title":"Are Large-Scale 3D Models Really Necessary for Accurate Visual Localization?","authors":"Torsten Sattler, A. Torii, Josef Sivic, M. Pollefeys, Hajime Taira, M. Okutomi, T. Pajdla","doi":"10.1109/CVPR.2017.654","DOIUrl":"https://doi.org/10.1109/CVPR.2017.654","url":null,"abstract":"Accurate visual localization is a key technology for autonomous navigation. 3D structure-based methods employ 3D models of the scene to estimate the full 6DOF pose of a camera very accurately. However, constructing (and extending) large-scale 3D models is still a significant challenge. In contrast, 2D image retrieval-based methods only require a database of geo-tagged images, which is trivial to construct and to maintain. They are often considered inaccurate since they only approximate the positions of the cameras. Yet, the exact camera pose can theoretically be recovered when enough relevant database images are retrieved. In this paper, we demonstrate experimentally that large-scale 3D models are not strictly necessary for accurate visual localization. We create reference poses for a large and challenging urban dataset. Using these poses, we show that combining image-based methods with local reconstructions results in a pose accuracy similar to the state-of-the-art structure-based methods. Our results suggest that we might want to reconsider the current approach for accurate large-scale localization.","PeriodicalId":6631,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"299 1","pages":"6175-6184"},"PeriodicalIF":0.0,"publicationDate":"2017-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74970466","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: 17