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Titlebook: Computer Vision – ECCV 2020; 16th European Confer Andrea Vedaldi,Horst Bischof,Jan-Michael Frahm Conference proceedings 2020 Springer Natur

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41#
發(fā)表于 2025-3-28 16:42:15 | 只看該作者
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發(fā)表于 2025-3-28 21:18:54 | 只看該作者
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發(fā)表于 2025-3-29 01:26:20 | 只看該作者
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發(fā)表于 2025-3-29 08:41:01 | 只看該作者
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發(fā)表于 2025-3-29 14:52:14 | 只看該作者
Deep Image Clustering with Category-Style Representation, propose a novel deep image clustering framework to learn a category-style latent representation in which the category information is disentangled from image style and can be directly used as the cluster assignment. To achieve this goal, mutual information maximization is applied to embed relevant i
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發(fā)表于 2025-3-29 17:42:25 | 只看該作者
48#
發(fā)表于 2025-3-29 22:16:18 | 只看該作者
Improving Monocular Depth Estimation by Leveraging Structural Awareness and Complementary Datasets,tructural information exploitation, which leads to inaccurate spatial layout, discontinuous surface, and ambiguous boundaries. In this paper, we tackle this problem in three aspects. First, to exploit the spatial relationship of visual features, we propose a structure-aware neural network with spati
49#
發(fā)表于 2025-3-30 01:19:23 | 只看該作者
BMBC: Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation,se a novel deep-learning-based video interpolation algorithm based on bilateral motion estimation. First, we develop the bilateral motion network with the bilateral cost volume to estimate bilateral motions accurately. Then, we approximate bi-directional motions to predict a different kind of bilate
50#
發(fā)表于 2025-3-30 08:05:16 | 只看該作者
Hard Negative Examples are Hard, but Useful,ser together in an embedding space than representations of images from different classes. Much work on triplet losses focuses on selecting the most useful triplets of images to consider, with strategies that select dissimilar examples from the same class or similar examples from different classes. T
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