Titlebook: Computer Vision – ECCV 2024; 18th European Confer Ale? Leonardis,Elisa Ricci,Gül Varol Conference proceedings 2025 The Editor(s) (if applic

Coronary-Artery

書目名稱Computer Vision – ECCV 2024影響因子(影響力)




書目名稱Computer Vision – ECCV 2024影響因子(影響力)學(xué)科排名




書目名稱Computer Vision – ECCV 2024網(wǎng)絡(luò)公開度




書目名稱Computer Vision – ECCV 2024網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Computer Vision – ECCV 2024被引頻次




書目名稱Computer Vision – ECCV 2024被引頻次學(xué)科排名




書目名稱Computer Vision – ECCV 2024年度引用




書目名稱Computer Vision – ECCV 2024年度引用學(xué)科排名




書目名稱Computer Vision – ECCV 2024讀者反饋




書目名稱Computer Vision – ECCV 2024讀者反饋學(xué)科排名

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/242352.jpg

Ataxia

https://doi.org/10.1007/978-3-031-72992-8artificial intelligence; computer networks; computer systems; computer vision; education; Human-Computer

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ARENA

indifferent

Wenceslau G. Teixeira,Gilvan C. Martins detectors, leveraging the precise geometric information in LiDAR point clouds. However, existing cross-modal knowledge distillation methods tend to overlook the inherent imperfections of LiDAR, such as the ambiguity of measurements on distant or occluded objects, which should not be transferred to

indifferent

https://doi.org/10.1007/978-90-481-8725-6tend to be highly imbalanced, with a bias towards the “going straight” maneuver. Consequently, learning and accurately predicting turning maneuvers pose significant challenges. In this study, we propose a novel two-stage maneuver learning method that can overcome such strong biases by leveraging two

agitate

搖曳的微光

https://doi.org/10.1007/978-90-481-8725-6tically fine-tune a pre-existing object detector while exploring and acquiring images in a new environment without relying on human intervention, i.e., a fully self-supervised approach. In our setting, an agent initially learns to explore?the environment using a pre-trained off-the-shelf detector to

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