IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision最新文献_第8页

Gradient based efficient feature selection 基于梯度的高效特征选择

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836102

S. Z. Gilani, F. Shafait, A. Mian

引用次数: 8

Viewpoint-independent book spine segmentation 独立于视点的书脊分割

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836066

L. Talker, Y. Moses

引用次数: 6

Detecting 3D geometric boundaries of indoor scenes under varying lighting 检测室内场景在不同光照条件下的三维几何边界

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836125

Jie Ni, Tim K. Marks, Oncel Tuzel, F. Porikli

引用次数: 1

3D Metric Rectification using Angle Regularity 使用角度规则的三维度量校正

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836121

Aamer Zaheer, Sohaib Khan

引用次数: 2

A combination of generative and discriminative models for fast unsupervised activity recognition from traffic scene videos 基于生成和判别模型的交通场景视频快速无监督活动识别

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836042

M. V. Krishna, Joachim Denzler

引用次数: 7

A CRF approach to fitting a generalized hand skeleton model 广义手骨架模型的CRF拟合方法

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836070

R. Mihail, G. Blomquist, Nathan Jacobs

引用次数: 5

Pedestrian detection in low resolution videos 低分辨率视频中的行人检测

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836038

Hisham Sager, W. Hoff

引用次数: 10

Hierarchical representation of videos with spatio-temporal fibers 具有时空纤维的视频分层表示

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836064

Ratnesh Kumar, G. Charpiat, M. Thonnat

引用次数: 2

Furniture-geek: Understanding fine-grained furniture attributes from freely associated text and tags 家具极客:从自由关联的文本和标签中理解细粒度家具属性

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836083

Vicente Ordonez, V. Jagadeesh, Wei Di, Anurag Bhardwaj, Robinson Piramuthu

{"title":"Furniture-geek: Understanding fine-grained furniture attributes from freely associated text and tags","authors":"Vicente Ordonez, V. Jagadeesh, Wei Di, Anurag Bhardwaj, Robinson Piramuthu","doi":"10.1109/WACV.2014.6836083","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836083","url":null,"abstract":"As the amount of user generated content on the internet grows, it becomes ever more important to come up with vision systems that learn directly from weakly annotated and noisy data. We leverage a large scale collection of user generated content comprising of images, tags and title/captions of furniture inventory from an e-commerce website to discover and categorize learnable visual attributes. Furniture categories have long been the quintessential example of why computer vision is hard, and we make one of the first attempts to understand them through a large scale weakly annotated dataset. We focus on a handful of furniture categories that are associated with a large number of fine-grained attributes. We propose a set of localized feature representations built on top of state-of-the-art computer vision representations originally designed for fine-grained object categorization. We report a thorough empirical characterization on the visual identifiability of various fine-grained attributes using these representations and show encouraging results on finding iconic images and on multi-attribute prediction.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"11 1","pages":"317-324"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84157800","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: 10

Data-driven exemplar model selection 数据驱动的范例模型选择

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836080

Ishan Misra, Abhinav Shrivastava, M. Hebert

{"title":"Data-driven exemplar model selection","authors":"Ishan Misra, Abhinav Shrivastava, M. Hebert","doi":"10.1109/WACV.2014.6836080","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836080","url":null,"abstract":"We consider the problem of discovering discriminative exemplars suitable for object detection. Due to the diversity in appearance in real world objects, an object detector must capture variations in scale, viewpoint, illumination etc. The current approaches do this by using mixtures of models, where each mixture is designed to capture one (or a few) axis of variation. Current methods usually rely on heuristics to capture these variations; however, it is unclear which axes of variation exist and are relevant to a particular task. Another issue is the requirement of a large set of training images to capture such variations. Current methods do not scale to large training sets either because of training time complexity [31] or test time complexity [26]. In this work, we explore the idea of compactly capturing task-appropriate variation from the data itself. We propose a two stage data-driven process, which selects and learns a compact set of exemplar models for object detection. These selected models have an inherent ranking, which can be used for anytime/budgeted detection scenarios. Another benefit of our approach (beyond the computational speedup) is that the selected set of exemplar models performs better than the entire set.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"10 1","pages":"339-346"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84218738","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