2017 IEEE International Conference on Computer Vision (ICCV)最新文献_第10页

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.88

Pau Panareda Busto, Juergen Gall

引用次数: 457

Ensemble Diffusion for Retrieval 用于检索的集合扩散

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.90

S. Bai, Zhichao Zhou, Jingdong Wang, X. Bai, Longin Jan Latecki, Q. Tian

{"title":"Ensemble Diffusion for Retrieval","authors":"S. Bai, Zhichao Zhou, Jingdong Wang, X. Bai, Longin Jan Latecki, Q. Tian","doi":"10.1109/ICCV.2017.90","DOIUrl":"https://doi.org/10.1109/ICCV.2017.90","url":null,"abstract":"As a postprocessing procedure, diffusion process has demonstrated its ability of substantially improving the performance of various visual retrieval systems. Whereas, great efforts are also devoted to similarity (or metric) fusion, seeing that only one individual type of similarity cannot fully reveal the intrinsic relationship between objects. This stimulates a great research interest of considering similarity fusion in the framework of diffusion process (i.e., fusion with diffusion) for robust retrieval.,,In this paper, we firstly revisit representative methods about fusion with diffusion, and provide new insights which are ignored by previous researchers. Then, observing that existing algorithms are susceptible to noisy similarities, the proposed Regularized Ensemble Diffusion (RED) is bundled with an automatic weight learning paradigm, so that the negative impacts of noisy similarities are suppressed. At last, we integrate several recently-proposed similarities with the proposed framework. The experimental results suggest that we can achieve new state-of-the-art performances on various retrieval tasks, including 3D shape retrieval on ModelNet dataset, and image retrieval on Holidays and Ukbench dataset.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"1 1","pages":"774-783"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84836487","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

Flip-Invariant Motion Representation 翻转不变运动表示

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.600

Takumi Kobayashi

引用次数: 7

A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework 基于稀疏编码的堆叠RNN异常检测研究

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.45

Weixin Luo, Wen Liu, Shenghua Gao

{"title":"A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework","authors":"Weixin Luo, Wen Liu, Shenghua Gao","doi":"10.1109/ICCV.2017.45","DOIUrl":"https://doi.org/10.1109/ICCV.2017.45","url":null,"abstract":"Motivated by the capability of sparse coding based anomaly detection, we propose a Temporally-coherent Sparse Coding (TSC) where we enforce similar neighbouring frames be encoded with similar reconstruction coefficients. Then we map the TSC with a special type of stacked Recurrent Neural Network (sRNN). By taking advantage of sRNN in learning all parameters simultaneously, the nontrivial hyper-parameter selection to TSC can be avoided, meanwhile with a shallow sRNN, the reconstruction coefficients can be inferred within a forward pass, which reduces the computational cost for learning sparse coefficients. The contributions of this paper are two-fold: i) We propose a TSC, which can be mapped to a sRNN which facilitates the parameter optimization and accelerates the anomaly prediction. ii) We build a very large dataset which is even larger than the summation of all existing dataset for anomaly detection in terms of both the volume of data and the diversity of scenes. Extensive experiments on both a toy dataset and real datasets demonstrate that our TSC based and sRNN based method consistently outperform existing methods, which validates the effectiveness of our method.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"56 1","pages":"341-349"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83111502","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: 484

Editable Parametric Dense Foliage from 3D Capture 可编辑的参数密集树叶从3D捕获

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.567

P. Beardsley, G. Chaurasia

引用次数: 9

Deep Free-Form Deformation Network for Object-Mask Registration 对象-掩码配准的深度自由变形网络

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.456

Haoyang Zhang, Xuming He

引用次数: 10

RPAN: An End-to-End Recurrent Pose-Attention Network for Action Recognition in Videos RPAN:一种用于视频动作识别的端到端递归姿态-注意网络

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.402

Wenbin Du, Yali Wang, Y. Qiao

{"title":"RPAN: An End-to-End Recurrent Pose-Attention Network for Action Recognition in Videos","authors":"Wenbin Du, Yali Wang, Y. Qiao","doi":"10.1109/ICCV.2017.402","DOIUrl":"https://doi.org/10.1109/ICCV.2017.402","url":null,"abstract":"Recent studies demonstrate the effectiveness of Recurrent Neural Networks (RNNs) for action recognition in videos. However, previous works mainly utilize video-level category as supervision to train RNNs, which may prohibit RNNs to learn complex motion structures along time. In this paper, we propose a recurrent pose-attention network (RPAN) to address this challenge, where we introduce a novel pose-attention mechanism to adaptively learn pose-related features at every time-step action prediction of RNNs. More specifically, we make three main contributions in this paper. Firstly, unlike previous works on pose-related action recognition, our RPAN is an end-toend recurrent network which can exploit important spatialtemporal evolutions of human pose to assist action recognition in a unified framework. Secondly, instead of learning individual human-joint features separately, our poseattention mechanism learns robust human-part features by sharing attention parameters partially on the semanticallyrelated human joints. These human-part features are then fed into the human-part pooling layer to construct a highlydiscriminative pose-related representation for temporal action modeling. Thirdly, one important byproduct of our RPAN is pose estimation in videos, which can be used for coarse pose annotation in action videos. We evaluate the proposed RPAN quantitatively and qualitatively on two popular benchmarks, i.e., Sub-JHMDB and PennAction. Experimental results show that RPAN outperforms the recent state-of-the-art methods on these challenging datasets.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"30 1","pages":"3745-3754"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81339891","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: 149

Object-Level Proposals 对象级的建议

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.527

Jianxiang Ma, Anlong Ming, Zilong Huang, Xinggang Wang, Yu Zhou

引用次数: 15

Supervision by Fusion: Towards Unsupervised Learning of Deep Salient Object Detector 融合监督:面向深度显著目标检测器的无监督学习

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.436

Dingwen Zhang, Junwei Han, Yu Zhang

{"title":"Supervision by Fusion: Towards Unsupervised Learning of Deep Salient Object Detector","authors":"Dingwen Zhang, Junwei Han, Yu Zhang","doi":"10.1109/ICCV.2017.436","DOIUrl":"https://doi.org/10.1109/ICCV.2017.436","url":null,"abstract":"In light of the powerful learning capability of deep neural networks (DNNs), deep (convolutional) models have been built in recent years to address the task of salient object detection. Although training such deep saliency models can significantly improve the detection performance, it requires large-scale manual supervision in the form of pixel-level human annotation, which is highly labor-intensive and time-consuming. To address this problem, this paper makes the earliest effort to train a deep salient object detector without using any human annotation. The key insight is “supervision by fusion”, i.e., generating useful supervisory signals from the fusion process of weak but fast unsupervised saliency models. Based on this insight, we combine an intra-image fusion stream and a inter-image fusion stream in the proposed framework to generate the learning curriculum and pseudo ground-truth for supervising the training of the deep salient object detector. Comprehensive experiments on four benchmark datasets demonstrate that our method can approach the same network trained with full supervision (within 2-5% performance gap) and, more encouragingly, even outperform a number of fully supervised state-of-the-art approaches.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"54 1","pages":"4068-4076"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85594968","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: 144

Makeup-Go: Blind Reversion of Portrait Edit 化妆- go:肖像编辑的盲目还原

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.482

Ying-Cong Chen, Xiaoyong Shen, Jiaya Jia

引用次数: 18