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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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楼主: COAX
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978-3-031-20079-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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,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 ..)
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,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
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,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)
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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
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,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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