2015 IEEE International Conference on Computer Vision (ICCV)最新文献_第7页

Category-Blind Human Action Recognition: A Practical Recognition System 类别盲人类动作识别:一种实用的识别系统

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.505

Wenbo Li, Longyin Wen, M. Chuah, Siwei Lyu

{"title":"Category-Blind Human Action Recognition: A Practical Recognition System","authors":"Wenbo Li, Longyin Wen, M. Chuah, Siwei Lyu","doi":"10.1109/ICCV.2015.505","DOIUrl":"https://doi.org/10.1109/ICCV.2015.505","url":null,"abstract":"Existing human action recognition systems for 3D sequences obtained from the depth camera are designed to cope with only one action category, either single-person action or two-person interaction, and are difficult to be extended to scenarios where both action categories co-exist. In this paper, we propose the category-blind human recognition method (CHARM) which can recognize a human action without making assumptions of the action category. In our CHARM approach, we represent a human action (either a single-person action or a two-person interaction) class using a co-occurrence of motion primitives. Subsequently, we classify an action instance based on matching its motion primitive co-occurrence patterns to each class representation. The matching task is formulated as maximum clique problems. We conduct extensive evaluations of CHARM using three datasets for single-person actions, two-person interactions, and their mixtures. Experimental results show that CHARM performs favorably when compared with several state-of-the-art single-person action and two-person interaction based methods without making explicit assumptions of action category.","PeriodicalId":6633,"journal":{"name":"2015 IEEE International Conference on Computer Vision (ICCV)","volume":"15 1","pages":"4444-4452"},"PeriodicalIF":0.0,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88804091","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: 67

Multi-View Complementary Hash Tables for Nearest Neighbor Search 多视图互补哈希表的最近邻搜索

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.132

Xianglong Liu, Lei Huang, Cheng Deng, Jiwen Lu, B. Lang

{"title":"Multi-View Complementary Hash Tables for Nearest Neighbor Search","authors":"Xianglong Liu, Lei Huang, Cheng Deng, Jiwen Lu, B. Lang","doi":"10.1109/ICCV.2015.132","DOIUrl":"https://doi.org/10.1109/ICCV.2015.132","url":null,"abstract":"Recent years have witnessed the success of hashing techniques in fast nearest neighbor search. In practice many applications (eg., visual search, object detection, image matching, etc.) have enjoyed the benefits of complementary hash tables and information fusion over multiple views. However, most of prior research mainly focused on compact hash code cleaning, and rare work studies how to build multiple complementary hash tables, much less to adaptively integrate information stemming from multiple views. In this paper we first present a novel multi-view complementary hash table method that learns complementarity hash tables from the data with multiple views. For single multi-view table, using exemplar based feature fusion, we approximate the inherent data similarities with a low-rank matrix, and learn discriminative hash functions in an efficient way. To build complementary tables and meanwhile maintain scalable training and fast out-of-sample extension, an exemplar reweighting scheme is introduced to update the induced low-rank similarity in the sequential table construction framework, which indeed brings mutual benefits between tables by placing greater importance on exemplars shared by mis-separated neighbors. Extensive experiments on three large-scale image datasets demonstrate that the proposed method significantly outperforms various naive solutions and state-of-the-art multi-table methods.","PeriodicalId":6633,"journal":{"name":"2015 IEEE International Conference on Computer Vision (ICCV)","volume":"120 1","pages":"1107-1115"},"PeriodicalIF":0.0,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77405825","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: 52

Fine-Grained Change Detection of Misaligned Scenes with Varied Illuminations 不同光照条件下不对齐场景的细粒度变化检测

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.149

Wei Feng, Fei-Peng Tian, Qian Zhang, N. Zhang, Liang Wan, Ji-zhou Sun

{"title":"Fine-Grained Change Detection of Misaligned Scenes with Varied Illuminations","authors":"Wei Feng, Fei-Peng Tian, Qian Zhang, N. Zhang, Liang Wan, Ji-zhou Sun","doi":"10.1109/ICCV.2015.149","DOIUrl":"https://doi.org/10.1109/ICCV.2015.149","url":null,"abstract":"Detecting fine-grained subtle changes among a scene is critically important in practice. Previous change detection methods, focusing on detecting large-scale significant changes, cannot do this well. This paper proposes a feasible end-to-end approach to this challenging problem. We start from active camera relocation that quickly relocates camera to nearly the same pose and position of the last time observation. To guarantee detection sensitivity and accuracy of minute changes, in an observation, we capture a group of images under multiple illuminations, which need only to be roughly aligned to the last time lighting conditions. Given two times observations, we formulate fine-grained change detection as a joint optimization problem of three related factors, i.e., normal-aware lighting difference, camera geometry correction flow, and real scene change mask. We solve the three factors in a coarse-to-fine manner and achieve reliable change decision by rank minimization. We build three real-world datasets to benchmark fine-grained change detection of misaligned scenes under varied multiple lighting conditions. Extensive experiments show the superior performance of our approach over state-of-the-art change detection methods and its ability to distinguish real scene changes from false ones caused by lighting variations.","PeriodicalId":6633,"journal":{"name":"2015 IEEE International Conference on Computer Vision (ICCV)","volume":"20 1","pages":"1260-1268"},"PeriodicalIF":0.0,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86937170","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: 32

Multiple Granularity Descriptors for Fine-Grained Categorization 用于细粒度分类的多粒度描述符

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.276

Dequan Wang, Zhiqiang Shen, Jie Shao, Wei Zhang, X. Xue, Zeyu Zhang

{"title":"Multiple Granularity Descriptors for Fine-Grained Categorization","authors":"Dequan Wang, Zhiqiang Shen, Jie Shao, Wei Zhang, X. Xue, Zeyu Zhang","doi":"10.1109/ICCV.2015.276","DOIUrl":"https://doi.org/10.1109/ICCV.2015.276","url":null,"abstract":"Fine-grained categorization, which aims to distinguish subordinate-level categories such as bird species or dog breeds, is an extremely challenging task. This is due to two main issues: how to localize discriminative regions for recognition and how to learn sophisticated features for representation. Neither of them is easy to handle if there is insufficient labeled data. We leverage the fact that a subordinate-level object already has other labels in its ontology tree. These \"free\" labels can be used to train a series of CNN-based classifiers, each specialized at one grain level. The internal representations of these networks have different region of interests, allowing the construction of multi-grained descriptors that encode informative and discriminative features covering all the grain levels. Our multiple granularity framework can be learned with the weakest supervision, requiring only image-level label and avoiding the use of labor-intensive bounding box or part annotations. Experimental results on three challenging fine-grained image datasets demonstrate that our approach outperforms state-of-the-art algorithms, including those requiring strong labels.","PeriodicalId":6633,"journal":{"name":"2015 IEEE International Conference on Computer Vision (ICCV)","volume":"58 1","pages":"2399-2406"},"PeriodicalIF":0.0,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85619473","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: 199

Automatic Thumbnail Generation Based on Visual Representativeness and Foreground Recognizability 基于视觉代表性和前景可识别性的缩略图自动生成

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.37

Jingwei Huang, Huarong Chen, Bin Wang, Stephen Lin

引用次数: 20

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

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

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

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

Airborne Three-Dimensional Cloud Tomography 航空三维云层析成像

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.386

Aviad Levis, Y. Schechner, Amit Aides, A. Davis

引用次数: 69