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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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發(fā)表于 2025-3-21 17:32:34 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
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/235/234197/234197.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; clustering; computer vision; face recognition; image classification; image proce
版次1
doihttps://doi.org/10.1007/978-3-030-01240-3
isbn_softcover978-3-030-01239-7
isbn_ebook978-3-030-01240-3Series 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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Ask, Acquire, and Attack: Data-Free UAP Generation Using Class Impressionsodel is a generic representation (in the input space) of the samples belonging to that category. Further, we present a neural network based generative model that utilizes the acquired class impressions to learn crafting UAPs. Experimental evaluation demonstrates that the learned generative model, (i
板凳
發(fā)表于 2025-3-22 01:42:36 | 只看該作者
Rendering Portraitures from Monocular Camera and Beyondth the refined estimation, we conduct depth and segmentation-aware blur rendering to the input image with a Conditional Random Field and image matting. In addition, we train a spatially-variant Recursive Neural Network to learn and accelerate this rendering process. We show that the proposed algorit
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A Scalable Exemplar-Based Subspace Clustering Algorithm for Class-Imbalanced Data from each subspace for expressing all data points even if the data are imbalanced. Our experiments demonstrate that the proposed method outperforms state-of-the-art subspace clustering methods in two large-scale image datasets that are imbalanced. We also demonstrate the effectiveness of our method
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發(fā)表于 2025-3-22 16:14:54 | 只看該作者
RCAA: Relational Context-Aware Agents for Person Searchch, we conduct extensive experiments on the large-scale Person Search benchmark dataset and achieve significant improvements over the compared approaches. It is also worth noting that the proposed model even performs better than traditional methods with perfect pedestrian detectors.
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發(fā)表于 2025-3-22 19:16:40 | 只看該作者
Distractor-Aware Siamese Networks for Visual Object Trackingorm incremental learning, which can effectively transfer the general embedding to the current video domain. In addition, we extend the proposed approach for long-term tracking by introducing a simple yet effective local-to-global search region strategy. Extensive experiments on benchmarks show that
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Learning Dynamic Memory Networks for Object Trackingine with the initial template. Unlike tracking-by-detection methods where the object’s information is maintained by the weight parameters of neural networks, which requires expensive online fine-tuning to be adaptable, our tracker runs completely feed-forward and adapts to the target’s appearance ch
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Face Super-Resolution Guided by Facial Component Heatmapslarity), but also middle-level information (., face structure) to further explore spatial constraints of facial components from LR inputs images. Therefore, we are able to super-resolve very small unaligned face images . with a large upscaling factor of 8., while preserving face structure. Extensive
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