2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)最新文献_第6页

Proximal Riemannian Pursuit for Large-Scale Trace-Norm Minimization 大规模迹范数最小化的近端黎曼追踪

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.633

Mingkui Tan, Shijie Xiao, Junbin Gao, Dong Xu, A. Hengel, Javen Qinfeng Shi

引用次数: 5

Prior-Less Compressible Structure from Motion 运动中的无先验可压缩结构

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.447

Chen Kong, S. Lucey

引用次数: 50

Scale-Aware Alignment of Hierarchical Image Segmentation 层次图像分割的尺度感知对齐

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.46

Yuhua Chen, Dengxin Dai, J. Pont-Tuset, L. Gool

{"title":"Scale-Aware Alignment of Hierarchical Image Segmentation","authors":"Yuhua Chen, Dengxin Dai, J. Pont-Tuset, L. Gool","doi":"10.1109/CVPR.2016.46","DOIUrl":"https://doi.org/10.1109/CVPR.2016.46","url":null,"abstract":"Image segmentation is a key component in many computer vision systems, and it is recovering a prominent spot in the literature as methods improve and overcome their limitations. The outputs of most recent algorithms are in the form of a hierarchical segmentation, which provides segmentation at different scales in a single tree-like structure. Commonly, these hierarchical methods start from some low-level features, and are not aware of the scale information of the different regions in them. As such, one might need to work on many different levels of the hierarchy to find the objects in the scene. This work tries to modify the existing hierarchical algorithm by improving their alignment, that is, by trying to modify the depth of the regions in the tree to better couple depth and scale. To do so, we first train a regressor to predict the scale of regions using mid-level features. We then define the anchor slice as the set of regions that better balance between over-segmentation and under-segmentation. The output of our method is an improved hierarchy, re-aligned by the anchor slice. To demonstrate the power of our method, we perform comprehensive experiments, which show that our method, as a post-processing step, can significantly improve the quality of the hierarchical segmentation representations, and ease the usage of hierarchical image segmentation to high-level vision tasks such as object segmentation. We also prove that the improvement generalizes well across different algorithms and datasets, with a low computational cost.","PeriodicalId":6515,"journal":{"name":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"45 1","pages":"364-372"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90800903","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: 30

MDL-CW: A Multimodal Deep Learning Framework with CrossWeights 基于交叉权重的多模态深度学习框架

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.285

Sarah Rastegar, M. Baghshah, H. Rabiee, Seyed Mohsen Shojaee

{"title":"MDL-CW: A Multimodal Deep Learning Framework with CrossWeights","authors":"Sarah Rastegar, M. Baghshah, H. Rabiee, Seyed Mohsen Shojaee","doi":"10.1109/CVPR.2016.285","DOIUrl":"https://doi.org/10.1109/CVPR.2016.285","url":null,"abstract":"Deep learning has received much attention as of the most powerful approaches for multimodal representation learning in recent years. An ideal model for multimodal data can reason about missing modalities using the available ones, and usually provides more information when multiple modalities are being considered. All the previous deep models contain separate modality-specific networks and find a shared representation on top of those networks. Therefore, they only consider high level interactions between modalities to find a joint representation for them. In this paper, we propose a multimodal deep learning framework (MDLCW) that exploits the cross weights between representation of modalities, and try to gradually learn interactions of the modalities in a deep network manner (from low to high level interactions). Moreover, we theoretically show that considering these interactions provide more intra-modality information, and introduce a multi-stage pre-training method that is based on the properties of multi-modal data. In the proposed framework, as opposed to the existing deep methods for multi-modal data, we try to reconstruct the representation of each modality at a given level, with representation of other modalities in the previous layer. Extensive experimental results show that the proposed model outperforms state-of-the-art information retrieval methods for both image and text queries on the PASCAL-sentence and SUN-Attribute databases.","PeriodicalId":6515,"journal":{"name":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"48 1","pages":"2601-2609"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79252352","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: 43

Face2Face: Real-Time Face Capture and Reenactment of RGB Videos Face2Face:实时人脸捕捉和再现RGB视频

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1145/3292039

Justus Thies, M. Zollhöfer, M. Stamminger, C. Theobalt, M. Nießner

{"title":"Face2Face: Real-Time Face Capture and Reenactment of RGB Videos","authors":"Justus Thies, M. Zollhöfer, M. Stamminger, C. Theobalt, M. Nießner","doi":"10.1145/3292039","DOIUrl":"https://doi.org/10.1145/3292039","url":null,"abstract":"We present a novel approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video). The source sequence is also a monocular video stream, captured live with a commodity webcam. Our goal is to animate the facial expressions of the target video by a source actor and re-render the manipulated output video in a photo-realistic fashion. To this end, we first address the under-constrained problem of facial identity recovery from monocular video by non-rigid model-based bundling. At run time, we track facial expressions of both source and target video using a dense photometric consistency measure. Reenactment is then achieved by fast and efficient deformation transfer between source and target. The mouth interior that best matches the re-targeted expression is retrieved from the target sequence and warped to produce an accurate fit. Finally, we convincingly re-render the synthesized target face on top of the corresponding video stream such that it seamlessly blends with the real-world illumination. We demonstrate our method in a live setup, where Youtube videos are reenacted in real time.","PeriodicalId":6515,"journal":{"name":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"15 1","pages":"2387-2395"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72617275","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: 1552

The Global Patch Collider 全局补丁碰撞器

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.21

Shenlong Wang, S. Fanello, Christoph Rhemann, S. Izadi, Pushmeet Kohli

{"title":"The Global Patch Collider","authors":"Shenlong Wang, S. Fanello, Christoph Rhemann, S. Izadi, Pushmeet Kohli","doi":"10.1109/CVPR.2016.21","DOIUrl":"https://doi.org/10.1109/CVPR.2016.21","url":null,"abstract":"This paper proposes a novel extremely efficient, fully-parallelizable, task-specific algorithm for the computation of global point-wise correspondences in images and videos. Our algorithm, the Global Patch Collider, is based on detecting unique collisions between image points using a collection of learned tree structures that act as conditional hash functions. In contrast to conventional approaches that rely on pairwise distance computation, our algorithm isolates distinctive pixel pairs that hit the same leaf during traversal through multiple learned tree structures. The split functions stored at the intermediate nodes of the trees are trained to ensure that only visually similar patches or their geometric or photometric transformed versions fall into the same leaf node. The matching process involves passing all pixel positions in the images under analysis through the tree structures. We then compute matches by isolating points that uniquely collide with each other ie. fell in the same empty leaf in multiple trees. Our algorithm is linear in the number of pixels but can be made constant time on a parallel computation architecture as the tree traversal for individual image points is decoupled. We demonstrate the efficacy of our method by using it to perform optical flow matching and stereo matching on some challenging benchmarks. Experimental results show that not only is our method extremely computationally efficient, but it is also able to match or outperform state of the art methods that are much more complex.","PeriodicalId":6515,"journal":{"name":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"1 1","pages":"127-135"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82143987","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: 34

Monocular Depth Estimation Using Neural Regression Forest 基于神经回归森林的单目深度估计

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.594

Anirban Roy, S. Todorovic

引用次数: 292

Canny Text Detector: Fast and Robust Scene Text Localization Algorithm Canny文本检测器:快速鲁棒的场景文本定位算法

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.388

Hojin Cho, Myung-Chul Sung, Bongjin Jun

引用次数: 104

Object Co-segmentation via Graph Optimized-Flexible Manifold Ranking 基于图优化-柔性流形排序的目标共分割

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.81

Rong Quan, Junwei Han, Dingwen Zhang, F. Nie

引用次数: 79

Structural Correlation Filter for Robust Visual Tracking 鲁棒视觉跟踪的结构相关滤波器

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.467

Si Liu, Tianzhu Zhang, Xiaochun Cao, Changsheng Xu

{"title":"Structural Correlation Filter for Robust Visual Tracking","authors":"Si Liu, Tianzhu Zhang, Xiaochun Cao, Changsheng Xu","doi":"10.1109/CVPR.2016.467","DOIUrl":"https://doi.org/10.1109/CVPR.2016.467","url":null,"abstract":"In this paper, we propose a novel structural correlation filter (SCF) model for robust visual tracking. The proposed SCF model takes part-based tracking strategies into account in a correlation filter tracker, and exploits circular shifts of all parts for their motion modeling to preserve target object structure. Compared with existing correlation filter trackers, our proposed tracker has several advantages: (1) Due to the part strategy, the learned structural correlation filters are less sensitive to partial occlusion, and have computational efficiency and robustness. (2) The learned filters are able to not only distinguish the parts from the background as the traditional correlation filters, but also exploit the intrinsic relationship among local parts via spatial constraints to preserve object structure. (3) The learned correlation filters not only make most parts share similar motion, but also tolerate outlier parts that have different motion. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed SCF tracking algorithm performs favorably against several state-of-the-art methods.","PeriodicalId":6515,"journal":{"name":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"535 ","pages":"4312-4320"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91450065","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: 165