期刊
COMPUTER VISION - ACCV 2018, PT II
卷 11362, 期 -, 页码 678-694出版社
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-030-20890-5_43
关键词
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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%. Our implementation is available at: https://www.inf.unihamburg.de/en/inst/ab/cv/people/wilms/attentionmask.
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