Asymmetric Contextual Modulation For Infrared Small Target Detection

2021 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2021)(2021)

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摘要
Single-frame infrared small target detection remains a challenge not only due to the scarcity of intrinsic target characteristics but also because of lacking a public dataset. In this paper, we first contribute an open dataset with high-quality annotations to advance the research in this field. We also propose an asymmetric contextual modulation module specially designed for detecting infrared small targets. To better highlight small targets, besides a top-down global contextual feedback, we supplement a bottom-up modulation pathway based on point-wise channel attention for exchanging high-level semantics and subtle low-level details. We report ablation studies and comparisons to state-of-the-art methods, where we find that our approach performs significantly better. Our dataset and code are available online(1).
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关键词
high-level semantics,intrinsic target characteristics,public dataset,open dataset,high-quality annotations,asymmetric contextual modulation module,global contextual feedback,point-wise channel attention,single-frame infrared small target detection,bottom-up modulation pathway
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