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Titlebook: Computer Vision – ECCV 2018; 15th European Confer Vittorio Ferrari,Martial Hebert,Yair Weiss Conference proceedings 2018 Springer Nature Sw

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書目名稱Computer Vision – ECCV 2018
副標(biāo)題15th European Confer
編輯Vittorio Ferrari,Martial Hebert,Yair Weiss
視頻videohttp://file.papertrans.cn/235/234196/234196.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computer Vision – ECCV 2018; 15th European Confer Vittorio Ferrari,Martial Hebert,Yair Weiss Conference proceedings 2018 Springer Nature Sw
描述The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018..The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical?sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization;?matching and recognition; video attention; and poster sessions..
出版日期Conference proceedings 2018
關(guān)鍵詞artificial intelligence; face images; face recognition; gesture recognition; image coding; image processi
版次1
doihttps://doi.org/10.1007/978-3-030-01261-8
isbn_softcover978-3-030-01260-1
isbn_ebook978-3-030-01261-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2018
The information of publication is updating

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Deep Expander Networks: Efficient Deep Networks from Graph Theoryy, allowing efficient information flow across layers. Inspired by these techniques, we propose to model connections between filters of a CNN using graphs which are simultaneously sparse and well connected. Sparsity results in efficiency while well connectedness can preserve the expressive power of t
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Visual Text Correction network that can simultaneously detect an inaccuracy in a sentence, and fix it by replacing the inaccurate word(s). Our method leverages the semantic interdependence of videos and words, as well as the short-term and long-term relations of the words in a sentence. Our proposed formulation can solve
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