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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Some Empirical Aspects of Entrepreneurshipcts. However, these two-stage “enumerate-and-select” methods suffer object feature ambiguity brought by dense proposals and low detection efficiency caused by the proposal enumeration procedure. In this study, we propose a sparse proposal evolution (SPE) approach, which advances WSOD from the two-st

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Robert D. Tollison,Richard E. Wagnertuned for object detection without needing to redesign a hierarchical backbone for pre-training. With minimal adaptations for fine-tuning, our plain-backbone detector can achieve competitive results. Surprisingly, we observe: (i) it is sufficient to build a simple feature pyramid from a single-scale

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https://doi.org/10.1007/978-1-349-17708-0 of a lightweight detector for deployment is often significantly different from a high-capacity detector. Thus, we investigate KD among heterogeneous teacher-student pairs for a wide application. We observe that the core difficulty for heterogeneous KD?(hetero-KD) is the significant semantic gap bet

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234261.jpg

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