Asymmetric Contextual Modulation for Infrared Small Target Detection
Yimian Dai, Yiquan Wu, Fei Zhou, Kobus Barnard
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- github.com/YimianDai/open-acmOfficialIn papermxnet★ 138
- github.com/YeRen123455/Infrared-Small-Target-Detectionpytorch★ 492
- github.com/xinyiying/basicirstdpytorch★ 286
- github.com/xdfai/sctransnetpytorch★ 182
- github.com/xinyiying/lespspytorch★ 131
- github.com/yeren123455/sirst-single-point-supervisionpytorch★ 24
Abstract
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.