2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)最新文献_第5页

Pull the Plug? Predicting If Computers or Humans Should Segment Images 拔掉插头?预测是计算机还是人类应该分割图像

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.48

D. Gurari, S. Jain, Margrit Betke, K. Grauman

{"title":"Pull the Plug? Predicting If Computers or Humans Should Segment Images","authors":"D. Gurari, S. Jain, Margrit Betke, K. Grauman","doi":"10.1109/CVPR.2016.48","DOIUrl":"https://doi.org/10.1109/CVPR.2016.48","url":null,"abstract":"Foreground object segmentation is a critical step for many image analysis tasks. While automated methods can produce high-quality results, their failures disappoint users in need of practical solutions. We propose a resource allocation framework for predicting how best to allocate a fixed budget of human annotation effort in order to collect higher quality segmentations for a given batch of images and automated methods. The framework is based on a proposed prediction module that estimates the quality of given algorithm-drawn segmentations. We demonstrate the value of the framework for two novel tasks related to \"pulling the plug\" on computer and human annotators. Specifically, we implement two systems that automatically decide, for a batch of images, when to replace 1) humans with computers to create coarse segmentations required to initialize segmentation tools and 2) computers with humans to create final, fine-grained segmentations. Experiments demonstrate the advantage of relying on a mix of human and computer efforts over relying on either resource alone for segmenting objects in three diverse datasets representing visible, phase contrast microscopy, and fluorescence microscopy images.","PeriodicalId":6515,"journal":{"name":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"16 1","pages":"382-391"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91022843","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: 27

A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation 同时估计反射率和照度的加权变分模型

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.304

Xueyang Fu, Delu Zeng, Yue Huang, Xiao-Ping Zhang, Xinghao Ding

引用次数: 642

Learning Aligned Cross-Modal Representations from Weakly Aligned Data 从弱对齐数据中学习对齐的跨模态表示

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.321

Lluís Castrejón, Y. Aytar, Carl Vondrick, H. Pirsiavash, A. Torralba

引用次数: 158

A Task-Oriented Approach for Cost-Sensitive Recognition 面向任务的成本敏感识别方法

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.242

Roozbeh Mottaghi, Hannaneh Hajishirzi, Ali Farhadi

引用次数: 4

Geometry-Informed Material Recognition 几何信息材料识别

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.172

Joseph DeGol, M. G. Fard, Derek Hoiem

引用次数: 43

Coordinating Multiple Disparity Proposals for Stereo Computation 协调多视差立体计算方案

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.436

Ang Li, Dapeng Chen, Yuanliu Liu, Zejian Yuan

引用次数: 29

A Holistic Approach to Cross-Channel Image Noise Modeling and Its Application to Image Denoising 跨通道图像噪声建模的整体方法及其在图像去噪中的应用

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.186

Seonghyeon Nam, Youngbae Hwang, Y. Matsushita, Seon Joo Kim

引用次数: 184

Deep Relative Distance Learning: Tell the Difference between Similar Vehicles 深度相对远程学习:区分相似车辆

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.238

Hongye Liu, Yonghong Tian, Yaowei Wang, Lu Pang, Tiejun Huang

{"title":"Deep Relative Distance Learning: Tell the Difference between Similar Vehicles","authors":"Hongye Liu, Yonghong Tian, Yaowei Wang, Lu Pang, Tiejun Huang","doi":"10.1109/CVPR.2016.238","DOIUrl":"https://doi.org/10.1109/CVPR.2016.238","url":null,"abstract":"The growing explosion in the use of surveillance cameras in public security highlights the importance of vehicle search from a large-scale image or video database. However, compared with person re-identification or face recognition, vehicle search problem has long been neglected by researchers in vision community. This paper focuses on an interesting but challenging problem, vehicle re-identification (a.k.a precise vehicle search). We propose a Deep Relative Distance Learning (DRDL) method which exploits a two-branch deep convolutional network to project raw vehicle images into an Euclidean space where distance can be directly used to measure the similarity of arbitrary two vehicles. To further facilitate the future research on this problem, we also present a carefully-organized largescale image database \"VehicleID\", which includes multiple images of the same vehicle captured by different realworld cameras in a city. We evaluate our DRDL method on our VehicleID dataset and another recently-released vehicle model classification dataset \"CompCars\" in three sets of experiments: vehicle re-identification, vehicle model verification and vehicle retrieval. Experimental results show that our method can achieve promising results and outperforms several state-of-the-art approaches.","PeriodicalId":6515,"journal":{"name":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"10 1","pages":"2167-2175"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88931445","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: 599

Learning Activity Progression in LSTMs for Activity Detection and Early Detection lstm在活动检测和早期检测中的学习活动进展

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.214

Shugao Ma, L. Sigal, S. Sclaroff

引用次数: 365

Discriminative Multi-modal Feature Fusion for RGBD Indoor Scene Recognition 基于多模态特征融合的RGBD室内场景识别

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.324

Hongyuan Zhu, Jean-Baptiste Weibel, Shijian Lu

{"title":"Discriminative Multi-modal Feature Fusion for RGBD Indoor Scene Recognition","authors":"Hongyuan Zhu, Jean-Baptiste Weibel, Shijian Lu","doi":"10.1109/CVPR.2016.324","DOIUrl":"https://doi.org/10.1109/CVPR.2016.324","url":null,"abstract":"RGBD scene recognition has attracted increasingly attention due to the rapid development of depth sensors and their wide application scenarios. While many research has been conducted, most work used hand-crafted features which are difficult to capture high-level semantic structures. Recently, the feature extracted from deep convolutional neural network has produced state-of-the-art results for various computer vision tasks, which inspire researchers to explore incorporating CNN learned features for RGBD scene understanding. On the other hand, most existing work combines rgb and depth features without adequately exploiting the consistency and complementary information between them. Inspired by some recent work on RGBD object recognition using multi-modal feature fusion, we introduce a novel discriminative multi-modal fusion framework for rgbd scene recognition for the first time which simultaneously considers the inter-and intra-modality correlation for all samples and meanwhile regularizing the learned features to be discriminative and compact. The results from the multimodal layer can be back-propagated to the lower CNN layers, hence the parameters of the CNN layers and multimodal layers are updated iteratively until convergence. Experiments on the recently proposed large scale SUN RGB-D datasets show that our method achieved the state-of-the-art without any image segmentation.","PeriodicalId":6515,"journal":{"name":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"18 1","pages":"2969-2976"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90067423","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: 97