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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 16:25:21 | 只看該作者
42#
發(fā)表于 2025-3-28 21:50:29 | 只看該作者
Tensor Low-Rank Reconstruction for Semantic Segmentation,o be effective for context information collection. Since the desired context consists of spatial-wise and channel-wise attentions, 3D representation is an appropriate formulation. However, these non-local methods describe 3D context information based on a 2D similarity matrix, where space compressio
43#
發(fā)表于 2025-3-28 23:20:02 | 只看該作者
Attentive Normalization,e modules, however. In this paper, we propose a light-weight integration between the two schema and present Attentive Normalization (AN). Instead of learning a single affine transformation, AN learns a mixture of affine transformations and utilizes their weighted-sum as the final affine transformati
44#
發(fā)表于 2025-3-29 04:43:49 | 只看該作者
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發(fā)表于 2025-3-29 09:39:02 | 只看該作者
46#
發(fā)表于 2025-3-29 15:28:10 | 只看該作者
47#
發(fā)表于 2025-3-29 17:06:15 | 只看該作者
Caption-Supervised Face Recognition: Training a State-of-the-Art Face Model Without Manual Annotatis built on top of millions of annotated samples. However, as we endeavor to take the performance to the next level, the reliance on annotated data becomes a major obstacle. We desire to explore an alternative approach, namely using captioned images for training, as an attempt to mitigate this diffic
48#
發(fā)表于 2025-3-29 22:19:03 | 只看該作者
Unselfie: Translating Selfies to Neutral-Pose Portraits in the Wild,require specialized equipment or a third-party photographer. However, in selfies, constraints such as human arm length often make the body pose look unnatural. To address this issue, we introduce ., a novel photographic transformation that automatically translates a selfie into a neutral-pose portra
49#
發(fā)表于 2025-3-30 00:59:39 | 只看該作者
https://doi.org/10.1007/978-3-319-93411-2Network security; Privacy; Anonymity; Cryptography; Security and privacy for big data; Security and priva
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