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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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樓主: Cleveland
11#
發(fā)表于 2025-3-23 09:52:36 | 只看該作者
12#
發(fā)表于 2025-3-23 17:36:28 | 只看該作者
TENet: Triple Excitation Network for Video Salient Object Detection,l and temporal features of VSOD. Furthermore, our semi-curriculum learning design enables the first online refinement strategy for VSOD, which allows exciting and boosting saliency responses during testing without re-training. The proposed triple excitations can easily plug in different VSOD methods
13#
發(fā)表于 2025-3-23 20:34:11 | 只看該作者
14#
發(fā)表于 2025-3-24 00:17:25 | 只看該作者
CLIFFNet for Monocular Depth Estimation with Hierarchical Embedding Loss,more reliable high-level cues. Through end-to-end training, CLIFFNet can learn to select the optimal combinations of low-level and high-level features, leading to more effective cross level feature fusion. When trained using the proposed hierarchical loss, CLIFFNet sets a new state of the art on pop
15#
發(fā)表于 2025-3-24 06:00:14 | 只看該作者
Conference proceedings 2020g; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..?..?.
16#
發(fā)表于 2025-3-24 08:37:45 | 只看該作者
0302-9743 processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..?..?.978-3-030-58557-0978-3-030-58558-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
17#
發(fā)表于 2025-3-24 13:25:48 | 只看該作者
18#
發(fā)表于 2025-3-24 15:27:09 | 只看該作者
Stream Regulation in Great Britain on four standard datasets and three backbones demonstrate that combining our centroid-based alignment loss results in absolute accuracy improvements of 4.4%, 1.2%, and 6.2% in 5-shot learning over the state of the art for object recognition, fine-grained classification, and cross-domain adaptation, respectively.
19#
發(fā)表于 2025-3-24 22:06:17 | 只看該作者
The Effects of Light on the Breeding Seasonhigher accuracy when retrieving similar poses across different camera views, in comparison with 2D-to-3D pose lifting models. We also demonstrate the effectiveness of applying our embeddings to view-invariant action recognition and video alignment. Our code is available at ..
20#
發(fā)表于 2025-3-25 01:48:30 | 只看該作者
The Effects of Light on the Onset of Pubertyoving quantitative measures of both kinematic and dynamic plausibility. We demonstrate our method on character animation and pose estimation tasks on dynamic motions of dancing and sports with complex contact patterns.
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