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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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51#
發(fā)表于 2025-3-30 11:24:07 | 只看該作者
978-3-031-20079-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
52#
發(fā)表于 2025-3-30 13:53:07 | 只看該作者
53#
發(fā)表于 2025-3-30 18:21:41 | 只看該作者
,DFNet: Enhance Absolute Pose Regression with?Direct Feature Matching,door and outdoor scenes. Hence, our method achieves a state-of-the-art accuracy by outperforming existing single-image APR methods by as much as 56%, comparable to 3D structure-based methods. (The code is available in ..)
54#
發(fā)表于 2025-3-30 22:44:26 | 只看該作者
,PillarNet: Real-Time and?High-Performance Pillar-Based 3D Object Detection,nd compatible with classical 2D CNN backbones, such as VGGNet and ResNet. Additionally, PillarNet benefits from our designed orientation-decoupled IoU regression loss along with the IoU-aware prediction branch. Extensive experimental results on the large-scale nuScenes Dataset and Waymo Open Dataset
55#
發(fā)表于 2025-3-31 02:01:49 | 只看該作者
,Robust Object Detection with?Inaccurate Bounding Boxes,bject-aware instance extension. The former aims to select accurate instances for training, instead of directly using inaccurate box annotations. The latter focuses on generating high-quality instances for selection. Extensive experiments on synthetic noisy datasets (., noisy PASCAL VOC and MS-COCO)
56#
發(fā)表于 2025-3-31 08:56:20 | 只看該作者
57#
發(fā)表于 2025-3-31 10:34:40 | 只看該作者
58#
發(fā)表于 2025-3-31 14:35:37 | 只看該作者
Towards Accurate Active Camera Localization,challenging localization scenarios from both synthetic and scanned real-world indoor scenes. Experimental results demonstrate that our algorithm outperforms both the state-of-the-art Markov Localization based approach and other compared approaches on the fine-scale camera pose accuracy. Code and dat
59#
發(fā)表于 2025-3-31 20:18:59 | 只看該作者
60#
發(fā)表于 2025-3-31 23:59:51 | 只看該作者
,Improving the?Intra-class Long-Tail in?3D Detection via?Rare Example Mining, active learning based on the criteria of uncertainty, difficulty, or diversity. In this study, we identify a new conceptual dimension - rareness - to mine new data for improving the long-tail performance of models. We show that rareness, as opposed to difficulty, is the key to data-centric improvem
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