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Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app

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樓主: ominous
51#
發(fā)表于 2025-3-30 11:13:02 | 只看該作者
The Economics of Small Businessesriety of advanced one-stage detection architectures. Specifically, on the COCO dataset, our method achieves between +3.1% and +4.6% AP improvement using ResNet-101 and ResNet-50 as the teacher and student backbones, respectively. On the CrowdHuman dataset, we achieve +3.2% and +2.0% improvements in
52#
發(fā)表于 2025-3-30 13:24:15 | 只看該作者
53#
發(fā)表于 2025-3-30 16:39:42 | 只看該作者
54#
發(fā)表于 2025-3-30 22:57:58 | 只看該作者
Small Business in German Manufacturingr gradient-based optimization. CPO is lightweight and achieves effective localization in all tested scenarios, showing stable performance despite scene changes, repetitive structures, or featureless regions, which are typical challenges for visual localization with perspective cameras.
55#
發(fā)表于 2025-3-31 02:02:18 | 只看該作者
56#
發(fā)表于 2025-3-31 07:12:41 | 只看該作者
https://doi.org/10.1007/978-94-015-7854-7en pixel in the camera view. Finally, using the weight maps, the total scene-level count is obtained as a simple weighted sum of the density maps for the camera views. Experiments are conducted on several multi-view counting datasets, and promising performance is achieved compared to calibrated MVCC
57#
發(fā)表于 2025-3-31 10:03:39 | 只看該作者
David S. Evans,Linda S. Leighton domain. Benefiting from the end-to-end framework that provides richer information of the pseudo labels, we propose the . strategy to take instance-level pseudo confidences into account and improve the effectiveness of the target-domain training process. Moreover, the . strategy and . are proposed t
58#
發(fā)表于 2025-3-31 16:38:05 | 只看該作者
59#
發(fā)表于 2025-3-31 20:08:32 | 只看該作者
Robert D. Tollison,Richard E. Wagneriscriminator (AID) and Consistent Features with Reconstruction (CFR) to ensure a reliable robustness. Extensive experiments on PASCAL VOC and MS-COCO demonstrate that our model effectively disentangles gradients and significantly enhances the detection robustness with maintaining the detection abili
60#
發(fā)表于 2025-4-1 00:38:59 | 只看該作者
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