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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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31#
發(fā)表于 2025-3-26 22:45:42 | 只看該作者
The Optimal Replacement Policy,o excavate informative parts of depth cues from the channel and spatial views. This fuses RGB and depth modalities in a complementary way. Our simple yet efficient architecture, dubbed .ifurcated .ackbone .trategy .work (.), is backbone independent and outperforms 18 SOTAs on seven challenging datasets using four metrics.
32#
發(fā)表于 2025-3-27 03:52:40 | 只看該作者
Weakly-Supervised Cell Tracking via Backward-and-Forward Propagation, of cell positions in the outputs of co-detection CNN. Experiments demonstrated that the proposed method can associate cells by analyzing co-detection CNN. Even though the method uses only weak supervision, the performance of our method was almost the same as the state-of-the-art supervised method. Code is publicly available in ..
33#
發(fā)表于 2025-3-27 08:59:48 | 只看該作者
34#
發(fā)表于 2025-3-27 10:17:18 | 只看該作者
35#
發(fā)表于 2025-3-27 16:55:31 | 只看該作者
36#
發(fā)表于 2025-3-27 18:35:46 | 只看該作者
Conference proceedings 2020n, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic..The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with top
37#
發(fā)表于 2025-3-27 23:23:00 | 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..?..?.
38#
發(fā)表于 2025-3-28 05:03:50 | 只看該作者
39#
發(fā)表于 2025-3-28 06:40:50 | 只看該作者
40#
發(fā)表于 2025-3-28 14:11:45 | 只看該作者
Single Image Super-Resolution via a Holistic Attention Network,ons of each channel to selectively capture more informative features. Extensive experiments demonstrate that the proposed HAN performs favorably against the state-of-the-art single image super-resolution approaches.
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