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Titlebook: Computer Vision – ECCV 2018 Workshops; Munich, Germany, Sep Laura Leal-Taixé,Stefan Roth Conference proceedings 2019 Springer Nature Switze

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書目名稱Computer Vision – ECCV 2018 Workshops
副標(biāo)題Munich, Germany, Sep
編輯Laura Leal-Taixé,Stefan Roth
視頻videohttp://file.papertrans.cn/235/234200/234200.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computer Vision – ECCV 2018 Workshops; Munich, Germany, Sep Laura Leal-Taixé,Stefan Roth Conference proceedings 2019 Springer Nature Switze
描述The six-volume set comprising the LNCS volumes 11129-11134 constitutes the refereed proceedings of the workshops that took place in?conjunction with the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.43 workshops from 74 workshops proposals were selected for inclusion in the proceedings. The workshop topics present a good?orchestration of new trends and traditional issues, built bridges into neighboring fields, and discuss fundamental technologies and?novel applications..
出版日期Conference proceedings 2019
關(guān)鍵詞computer vision; machine learning; vision for robotics; natural language processing; activity recognitio
版次1
doihttps://doi.org/10.1007/978-3-030-11015-4
isbn_softcover978-3-030-11014-7
isbn_ebook978-3-030-11015-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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https://doi.org/10.1057/9780230389113ew BrandFashion dataset which is richly annotated at different granularities. Experimental results demonstrate that the proposed method is very effective in capturing a tiered similarity search space and outperforms the state-of-the-art fashion search methods.
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English Electric — A Failure of Strategyyed in Creative Adversarial Networks. In the end, about 61% of our images are thought to be created by human designers rather than by a computer while also being considered original per our human subject experiments, and our proposed loss scores the highest compared to existing losses in both novelt
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https://doi.org/10.1007/978-1-349-26582-4aze behavior correlates to the type of the action under execution. This information is then used to plan the leader’s actions in order to sustain the leader/follower alignment in the social interaction..The model of the leader’s gaze behavior and the alignment of the intentions is evaluated in a hum
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DesIGN: Design Inspiration from Generative Networksyed in Creative Adversarial Networks. In the end, about 61% of our images are thought to be created by human designers rather than by a computer while also being considered original per our human subject experiments, and our proposed loss scores the highest compared to existing losses in both novelt
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