2015 IEEE International Conference on Computer Vision (ICCV)最新文献_第9页

Learning Spatially Regularized Correlation Filters for Visual Tracking 学习用于视觉跟踪的空间正则化相关滤波器

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.490

Martin Danelljan, Gustav Häger, F. Khan, M. Felsberg

{"title":"Learning Spatially Regularized Correlation Filters for Visual Tracking","authors":"Martin Danelljan, Gustav Häger, F. Khan, M. Felsberg","doi":"10.1109/ICCV.2015.490","DOIUrl":"https://doi.org/10.1109/ICCV.2015.490","url":null,"abstract":"Robust and accurate visual tracking is one of the most challenging computer vision problems. Due to the inherent lack of training data, a robust approach for constructing a target appearance model is crucial. Recently, discriminatively learned correlation filters (DCF) have been successfully applied to address this problem for tracking. These methods utilize a periodic assumption of the training samples to efficiently learn a classifier on all patches in the target neighborhood. However, the periodic assumption also introduces unwanted boundary effects, which severely degrade the quality of the tracking model. We propose Spatially Regularized Discriminative Correlation Filters (SRDCF) for tracking. A spatial regularization component is introduced in the learning to penalize correlation filter coefficients depending on their spatial location. Our SRDCF formulation allows the correlation filters to be learned on a significantly larger set of negative training samples, without corrupting the positive samples. We further propose an optimization strategy, based on the iterative Gauss-Seidel method, for efficient online learning of our SRDCF. Experiments are performed on four benchmark datasets: OTB-2013, ALOV++, OTB-2015, and VOT2014. Our approach achieves state-of-the-art results on all four datasets. On OTB-2013 and OTB-2015, we obtain an absolute gain of 8.0% and 8.2% respectively, in mean overlap precision, compared to the best existing trackers.","PeriodicalId":6633,"journal":{"name":"2015 IEEE International Conference on Computer Vision (ICCV)","volume":"67 1","pages":"4310-4318"},"PeriodicalIF":0.0,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91009967","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: 1749

Simultaneous Foreground Detection and Classification with Hybrid Features 混合特征的同时前景检测与分类

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.378

Jaemyun Kim, Adín Ramírez Rivera, Byungyong Ryu, O. Chae

引用次数: 13

Projection onto the Manifold of Elongated Structures for Accurate Extraction 投影到流形上的细长结构精确提取

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.44

A. Sironi, V. Lepetit, P. Fua

引用次数: 36

A Comprehensive Multi-Illuminant Dataset for Benchmarking of the Intrinsic Image Algorithms 一个综合的多光源数据集用于内在图像算法的基准测试

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.28

Shida Beigpour, A. Kolb, Sven Kunz

引用次数: 13

Fast and Effective L0 Gradient Minimization by Region Fusion 基于区域融合的快速有效L0梯度最小化算法

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.32

Nguyen Ho Man Rang, M. S. Brown

引用次数: 62

Single Image Pop-Up from Discriminatively Learned Parts 单个图像弹出从判别学习部分

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.112

Menglong Zhu, Xiaowei Zhou, Kostas Daniilidis

引用次数: 23

Component-Wise Modeling of Articulated Objects 铰接对象的组件建模

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.268

Valsamis Ntouskos, Marta Sanzari, B. Cafaro, F. Nardi, Fabrizio Natola, F. Pirri, M. A. Garcia

引用次数: 18

Learning Deep Representation with Large-Scale Attributes 学习大规模属性的深度表示

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.220

Wanli Ouyang, Hongyang Li, Xingyu Zeng, Xiaogang Wang

{"title":"Learning Deep Representation with Large-Scale Attributes","authors":"Wanli Ouyang, Hongyang Li, Xingyu Zeng, Xiaogang Wang","doi":"10.1109/ICCV.2015.220","DOIUrl":"https://doi.org/10.1109/ICCV.2015.220","url":null,"abstract":"Learning strong feature representations from large scale supervision has achieved remarkable success in computer vision as the emergence of deep learning techniques. It is driven by big visual data with rich annotations. This paper contributes a large-scale object attribute database that contains rich attribute annotations (over 300 attributes) for ~180k samples and 494 object classes. Based on the ImageNet object detection dataset, it annotates the rotation, viewpoint, object part location, part occlusion, part existence, common attributes, and class-specific attributes. Then we use this dataset to train deep representations and extensively evaluate how these attributes are useful on the general object detection task. In order to make better use of the attribute annotations, a deep learning scheme is proposed by modeling the relationship of attributes and hierarchically clustering them into semantically meaningful mixture types. Experimental results show that the attributes are helpful in learning better features and improving the object detection accuracy by 2.6% in mAP on the ILSVRC 2014 object detection dataset and 2.4% in mAP on PASCAL VOC 2007 object detection dataset. Such improvement is well generalized across datasets.","PeriodicalId":6633,"journal":{"name":"2015 IEEE International Conference on Computer Vision (ICCV)","volume":"42 1","pages":"1895-1903"},"PeriodicalIF":0.0,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75493703","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: 23

Unsupervised Domain Adaptation for Zero-Shot Learning 零射击学习的无监督域自适应

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.282

Elyor Kodirov, T. Xiang, Zhenyong Fu, S. Gong

{"title":"Unsupervised Domain Adaptation for Zero-Shot Learning","authors":"Elyor Kodirov, T. Xiang, Zhenyong Fu, S. Gong","doi":"10.1109/ICCV.2015.282","DOIUrl":"https://doi.org/10.1109/ICCV.2015.282","url":null,"abstract":"Zero-shot learning (ZSL) can be considered as a special case of transfer learning where the source and target domains have different tasks/label spaces and the target domain is unlabelled, providing little guidance for the knowledge transfer. A ZSL method typically assumes that the two domains share a common semantic representation space, where a visual feature vector extracted from an image/video can be projected/embedded using a projection function. Existing approaches learn the projection function from the source domain and apply it without adaptation to the target domain. They are thus based on naive knowledge transfer and the learned projections are prone to the domain shift problem. In this paper a novel ZSL method is proposed based on unsupervised domain adaptation. Specifically, we formulate a novel regularised sparse coding framework which uses the target domain class labels' projections in the semantic space to regularise the learned target domain projection thus effectively overcoming the projection domain shift problem. Extensive experiments on four object and action recognition benchmark datasets show that the proposed ZSL method significantly outperforms the state-of-the-arts.","PeriodicalId":6633,"journal":{"name":"2015 IEEE International Conference on Computer Vision (ICCV)","volume":"43 1","pages":"2452-2460"},"PeriodicalIF":0.0,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73580029","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: 375

kNN Hashing with Factorized Neighborhood Representation 具有分解邻域表示的kNN哈希

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.131

Kun Ding, Chunlei Huo, Bin Fan, Chunhong Pan

引用次数: 14