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Titlebook: Computer Vision –ACCV 2016; 13th Asian Conferenc Shang-Hong Lai,Vincent Lepetit,Yoichi Sato Conference proceedings 2017 Springer Internatio

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發(fā)表于 2025-3-21 19:31:46 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Computer Vision –ACCV 2016
副標題13th Asian Conferenc
編輯Shang-Hong Lai,Vincent Lepetit,Yoichi Sato
視頻videohttp://file.papertrans.cn/235/234116/234116.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computer Vision –ACCV 2016; 13th Asian Conferenc Shang-Hong Lai,Vincent Lepetit,Yoichi Sato Conference proceedings 2017 Springer Internatio
描述.The five-volume set LNCS 10111-10115 constitutes the thoroughly refereed post-conference proceedings of the 13th Asian Conference on Computer Vision, ACCV 2016, held in Taipei, Taiwan, in November 2016. ..The total of 143 contributions presented in these volumes was carefully reviewed and selected from 479 submissions. The papers are organized in topical sections on Segmentation and Classification; Segmentation and Semantic Segmentation; Dictionary Learning, Retrieval, and Clustering; Deep Learning; People Tracking and Action Recognition; People and Actions; Faces; Computational Photography; Face and Gestures; Image Alignment; Computational Photography and Image Processing; Language and Video; 3D Computer Vision; Image Attributes, Language, and Recognition; Video Understanding; and 3D Vision..
出版日期Conference proceedings 2017
關鍵詞3D vision; clustering; computer vision; image processing; neural networks; action recognition; computation
版次1
doihttps://doi.org/10.1007/978-3-319-54181-5
isbn_softcover978-3-319-54180-8
isbn_ebook978-3-319-54181-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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The Development of the Vertebrate Retinan an instance of the class, and further process good proposals to produce an accurate object cutout mask. This amounts to an automatic end-to-end pipeline for catergory-specific object cutout. We evaluate our approach on segmentation benchmark datasets, and show that it significantly outperforms the
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Deep Supervised Hashing with Triplet Labelsmaximizing the likelihood of pairwise similarities. Inspired by DPSH?[.], we propose a triplet label based deep hashing method which aims to maximize the likelihood of the given triplet labels. Experimental results show that our method outperforms all the baselines on CIFAR-10 and NUS-WIDE datasets,
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Boosting Zero-Shot Image Classification via Pairwise Relationship LearningExtensive experiments validate the effectiveness of our method: with the properly learned pairwise relationships, we successfully boost the mean class accuracy of DAP on two standard benchmarks for the ZSIC problem, . and ., from . to . and . to ., respectively. Besides, experimental results on the
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FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture competitive results with the state-of-the-art methods on the challenging SUN RGB-D benchmark obtaining 76.27% global accuracy, 48.30% average class accuracy and 37.29% average intersection-over-union score.
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