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- W2901783412 abstract "We propose a novel approach for class-agnostic object proposal generation, which is efficient and especially well-suited to detect small objects. Efficiency is achieved by scale-specific objectness attention maps which focus the processing on promising parts of the image and reduce the amount of sampled windows strongly. This leads to a system, which is $33%$ faster than the state-of-the-art and clearly outperforming state-of-the-art in terms of average recall. Secondly, we add a module for detecting small objects, which are often missed by recent models. We show that this module improves the average recall for small objects by about $53%$." @default.
- W2901783412 created "2018-11-29" @default.
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- W2901783412 date "2018-11-21" @default.
- W2901783412 modified "2023-09-27" @default.
- W2901783412 title "AttentionMask: Attentive, Efficient Object Proposal Generation Focusing on Small Objects" @default.
- W2901783412 hasPublicationYear "2018" @default.
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