2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)最新文献_第7页

A Multi-level Contextual Model for Person Recognition in Photo Albums 相册中人识别的多层次语境模型

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.145

Haoxiang Li, Jonathan Brandt, Zhe L. Lin, Xiaohui Shen, G. Hua

{"title":"A Multi-level Contextual Model for Person Recognition in Photo Albums","authors":"Haoxiang Li, Jonathan Brandt, Zhe L. Lin, Xiaohui Shen, G. Hua","doi":"10.1109/CVPR.2016.145","DOIUrl":"https://doi.org/10.1109/CVPR.2016.145","url":null,"abstract":"In this work, we present a new framework for person recognition in photo albums that exploits contextual cues at multiple levels, spanning individual persons, individual photos, and photo groups. Through experiments, we show that the information available at each of these distinct contextual levels provides complementary cues as to person identities. At the person level, we leverage clothing and body appearance in addition to facial appearance, and to compensate for instances where the faces are not visible. At the photo level we leverage a learned prior on the joint distribution of identities on the same photo to guide the identity assignments. Going beyond a single photo, we are able to infer natural groupings of photos with shared context in an unsupervised manner. By exploiting this shared contextual information, we are able to reduce the identity search space and exploit higher intra-personal appearance consistency within photo groups. Our new framework enables efficient use of these complementary multi-level contextual cues to improve overall recognition rates on the photo album person recognition task, as demonstrated through state-of-theart results on a challenging public dataset. Our results outperform competing methods by a significant margin, while being computationally efficient and practical in a real world application.","PeriodicalId":6515,"journal":{"name":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"12 1","pages":"1297-1305"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91156869","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

DeepHand: Robust Hand Pose Estimation by Completing a Matrix Imputed with Deep Features DeepHand:基于深度特征完成矩阵的鲁棒手部姿态估计

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.450

Ayan Sinha, Chiho Choi, K. Ramani

引用次数: 151

Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis 用于人类行为分析的多模态自发情绪语料库

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.374

Zheng Zhang, J. Girard, Yue Wu, Xing Zhang, Peng Liu, U. Ciftci, Shaun J. Canavan, M. Reale, Andy Horowitz, Huiyuan Yang, J. Cohn, Q. Ji, L. Yin

{"title":"Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis","authors":"Zheng Zhang, J. Girard, Yue Wu, Xing Zhang, Peng Liu, U. Ciftci, Shaun J. Canavan, M. Reale, Andy Horowitz, Huiyuan Yang, J. Cohn, Q. Ji, L. Yin","doi":"10.1109/CVPR.2016.374","DOIUrl":"https://doi.org/10.1109/CVPR.2016.374","url":null,"abstract":"Emotion is expressed in multiple modalities, yet most research has considered at most one or two. This stems in part from the lack of large, diverse, well-annotated, multimodal databases with which to develop and test algorithms. We present a well-annotated, multimodal, multidimensional spontaneous emotion corpus of 140 participants. Emotion inductions were highly varied. Data were acquired from a variety of sensors of the face that included high-resolution 3D dynamic imaging, high-resolution 2D video, and thermal (infrared) sensing, and contact physiological sensors that included electrical conductivity of the skin, respiration, blood pressure, and heart rate. Facial expression was annotated for both the occurrence and intensity of facial action units from 2D video by experts in the Facial Action Coding System (FACS). The corpus further includes derived features from 3D, 2D, and IR (infrared) sensors and baseline results for facial expression and action unit detection. The entire corpus will be made available to the research community.","PeriodicalId":6515,"journal":{"name":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"8 1","pages":"3438-3446"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79878887","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: 299

Single Image Camera Calibration with Lenticular Arrays for Augmented Reality 基于透镜阵列的增强现实单图像相机标定

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.358

Ian Schillebeeckx, Robert Pless

引用次数: 10

Mnemonic Descent Method: A Recurrent Process Applied for End-to-End Face Alignment 助记下降法:一种用于端到端人脸对齐的循环过程

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.453

George Trigeorgis, Patrick Snape, M. Nicolaou, Epameinondas Antonakos, S. Zafeiriou

{"title":"Mnemonic Descent Method: A Recurrent Process Applied for End-to-End Face Alignment","authors":"George Trigeorgis, Patrick Snape, M. Nicolaou, Epameinondas Antonakos, S. Zafeiriou","doi":"10.1109/CVPR.2016.453","DOIUrl":"https://doi.org/10.1109/CVPR.2016.453","url":null,"abstract":"Cascaded regression has recently become the method of choice for solving non-linear least squares problems such as deformable image alignment. Given a sizeable training set, cascaded regression learns a set of generic rules that are sequentially applied to minimise the least squares problem. Despite the success of cascaded regression for problems such as face alignment and head pose estimation, there are several shortcomings arising in the strategies proposed thus far. Specifically, (a) the regressors are learnt independently, (b) the descent directions may cancel one another out and (c) handcrafted features (e.g., HoGs, SIFT etc.) are mainly used to drive the cascade, which may be sub-optimal for the task at hand. In this paper, we propose a combined and jointly trained convolutional recurrent neural network architecture that allows the training of an end-to-end to system that attempts to alleviate the aforementioned drawbacks. The recurrent module facilitates the joint optimisation of the regressors by assuming the cascades form a nonlinear dynamical system, in effect fully utilising the information between all cascade levels by introducing a memory unit that shares information across all levels. The convolutional module allows the network to extract features that are specialised for the task at hand and are experimentally shown to outperform hand-crafted features. We show that the application of the proposed architecture for the problem of face alignment results in a strong improvement over the current state-of-the-art.","PeriodicalId":6515,"journal":{"name":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"47 1","pages":"4177-4187"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87349045","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: 340

Learning Cross-Domain Landmarks for Heterogeneous Domain Adaptation 异构领域适应的跨领域标志学习

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.549

Yao-Hung Hubert Tsai, Yi-Ren Yeh, Y. Wang

引用次数: 144

Do It Yourself Hyperspectral Imaging with Everyday Digital Cameras 用日常数码相机自己做高光谱成像

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.270

Seoung Wug Oh, M. S. Brown, M. Pollefeys, Seon Joo Kim

引用次数: 70

6D Dynamic Camera Relocalization from Single Reference Image 从单个参考图像重新定位6D动态相机

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.439

Wei Feng, Fei-Peng Tian, Qian Zhang, Ji-zhou Sun

{"title":"6D Dynamic Camera Relocalization from Single Reference Image","authors":"Wei Feng, Fei-Peng Tian, Qian Zhang, Ji-zhou Sun","doi":"10.1109/CVPR.2016.439","DOIUrl":"https://doi.org/10.1109/CVPR.2016.439","url":null,"abstract":"Dynamic relocalization of 6D camera pose from single reference image is a costly and challenging task that requires delicate hand-eye calibration and precision positioning platform to do 3D mechanical rotation and translation. In this paper, we show that high-quality camera relocalization can be achieved in a much less expensive way. Based on inexpensive platform with unreliable absolute repositioning accuracy (ARA), we propose a hand-eye calibration free strategy to actively relocate camera into the same 6D pose that produces the input reference image, by sequentially correcting 3D relative rotation and translation. We theoretically prove that, by this strategy, both rotational and translational relative pose can be effectively reduced to zero, with bounded unknown hand-eye pose displacement. To conquer 3D rotation and translation ambiguity, this theoretical strategy is further revised to a practical relocalization algorithm with faster convergence rate and more reliability by jointly adjusting 3D relative rotation and translation. Extensive experiments validate the effectiveness and superior accuracy of the proposed approach on laboratory tests and challenging real-world applications.","PeriodicalId":6515,"journal":{"name":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"32 1","pages":"4049-4057"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89429716","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: 18

Constrained Deep Transfer Feature Learning and Its Applications 约束深度迁移特征学习及其应用

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.551

Yue Wu, Q. Ji

{"title":"Constrained Deep Transfer Feature Learning and Its Applications","authors":"Yue Wu, Q. Ji","doi":"10.1109/CVPR.2016.551","DOIUrl":"https://doi.org/10.1109/CVPR.2016.551","url":null,"abstract":"Feature learning with deep models has achieved impressive results for both data representation and classification for various vision tasks. Deep feature learning, however, typically requires a large amount of training data, which may not be feasible for some application domains. Transfer learning can be one of the approaches to alleviate this problem by transferring data from data-rich source domain to data-scarce target domain. Existing transfer learning methods typically perform one-shot transfer learning and often ignore the specific properties that the transferred data must satisfy. To address these issues, we introduce a constrained deep transfer feature learning method to perform simultaneous transfer learning and feature learning by performing transfer learning in a progressively improving feature space iteratively in order to better narrow the gap between the target domain and the source domain for effective transfer of the data from source domain to target domain. Furthermore, we propose to exploit the target domain knowledge and incorporate such prior knowledge as constraint during transfer learning to ensure that the transferred data satisfies certain properties of the target domain. To demonstrate the effectiveness of the proposed constrained deep transfer feature learning method, we apply it to thermal feature learning for eye detection by transferring from the visible domain. We also applied the proposed method for cross-view facial expression recognition as a second application. The experimental results demonstrate the effectiveness of the proposed method for both applications.","PeriodicalId":6515,"journal":{"name":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"1 1","pages":"5101-5109"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89681627","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: 34

CoMaL: Good Features to Match on Object Boundaries CoMaL:在对象边界上匹配的良好特征

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2016-06-27 DOI: 10.1109/CVPR.2016.43

Swarna Kamlam Ravindran, Anurag Mittal

引用次数: 7