2013 IEEE Conference on Computer Vision and Pattern Recognition最新文献_第10页

First-Person Activity Recognition: What Are They Doing to Me? 第一人称活动识别:他们对我做了什么?

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.352

M. Ryoo, L. Matthies

引用次数: 287

A Fast Approximate AIB Algorithm for Distributional Word Clustering 分布式词聚类的快速近似AIB算法

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.78

Lei Wang, Jianjia Zhang, Luping Zhou, W. Li

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引用次数: 1

Detection Evolution with Multi-order Contextual Co-occurrence 多阶上下文共现的检测进化

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.235

Guang Chen, Yuanyuan Ding, Jing Xiao, T. Han

{"title":"Detection Evolution with Multi-order Contextual Co-occurrence","authors":"Guang Chen, Yuanyuan Ding, Jing Xiao, T. Han","doi":"10.1109/CVPR.2013.235","DOIUrl":"https://doi.org/10.1109/CVPR.2013.235","url":null,"abstract":"Context has been playing an increasingly important role to improve the object detection performance. In this paper we propose an effective representation, Multi-Order Contextual co-Occurrence (MOCO), to implicitly model the high level context using solely detection responses from a baseline object detector. The so-called (1st-order) context feature is computed as a set of randomized binary comparisons on the response map of the baseline object detector. The statistics of the 1st-order binary context features are further calculated to construct a high order co-occurrence descriptor. Combining the MOCO feature with the original image feature, we can evolve the baseline object detector to a stronger context aware detector. With the updated detector, we can continue the evolution till the contextual improvements saturate. Using the successful deformable-part-model detector [13] as the baseline detector, we test the proposed MOCO evolution framework on the PASCAL VOC 2007 dataset [8] and Caltech pedestrian dataset [7]: The proposed MOCO detector outperforms all known state-of-the-art approaches, contextually boosting deformable part models (ver. 5) [13] by 3.3% in mean average precision on the PASCAL 2007 dataset. For the Caltech pedestrian dataset, our method further reduces the log-average miss rate from 48% to 46% and the miss rate at 1 FPPI from 25% to 23%, compared with the best prior art [6].","PeriodicalId":6343,"journal":{"name":"2013 IEEE Conference on Computer Vision and Pattern Recognition","volume":"4 1","pages":"1798-1805"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81398816","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: 93

Supervised Semantic Gradient Extraction Using Linear-Time Optimization 基于线性时间优化的监督语义梯度提取

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.364

Shulin Yang, Jue Wang, L. Shapiro

引用次数: 2

City-Scale Change Detection in Cadastral 3D Models Using Images 基于图像的地籍三维模型城市尺度变化检测

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.22

Aparna Taneja, Luca Ballan, M. Pollefeys

引用次数: 78

Physically Plausible 3D Scene Tracking: The Single Actor Hypothesis 物理上可信的3D场景跟踪:单演员假设

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.9

Nikolaos Kyriazis, Antonis A. Argyros

引用次数: 70

Sparse Quantization for Patch Description 补丁描述的稀疏量化

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.366

X. Boix, Michael Gygli, G. Roig, L. Gool

引用次数: 18

Class Generative Models Based on Feature Regression for Pose Estimation of Object Categories 基于特征回归的类生成模型用于目标类别姿态估计

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.103

Michele Fenzi, L. Leal-Taixé, B. Rosenhahn, J. Ostermann

引用次数: 36

Studying Relationships between Human Gaze, Description, and Computer Vision 研究人类凝视、描述和计算机视觉之间的关系

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.101

Kiwon Yun, Yifan Peng, D. Samaras, G. Zelinsky, Tamara L. Berg

引用次数: 88

Efficient Detector Adaptation for Object Detection in a Video 高效检测器自适应视频中目标检测

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.418

Pramod Sharma, R. Nevatia

引用次数: 38