Noise-Robust Box-Supervised Infrared Small Target Detection via Physics-Inspired Soft Label Optimization
Infrared small target detection (IRSTD) commonly relies on pixel-level mask supervision. Such annotations, however, are costly and inherently uncertain because infrared targets have blurred boundaries and weak textures. We formulate box-supervised IRSTD as a problem distinct from generic box-to-mask segmentation and point-supervised IRSTD. Its central challenge is to construct stable pixel-level soft supervision from highly contaminated boxes. To this end, we propose Hotspot-Anchored Label Optimization (HALO). HALO localizes a radiometric anchor inside each box under local background-statistic
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- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%DietrichGebert/ponytail →
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- LinkedLinked via arxiv author · 85%Xizhe Zhang →
“Noise-Robust Box-Supervised Infrared Small Target Detection via Physics-Inspired Soft Label Optimization”
- LinkedLinked via arxiv author · 85%Yifan Shi →
“Noise-Robust Box-Supervised Infrared Small Target Detection via Physics-Inspired Soft Label Optimization”
- LinkedLinked via arxiv author · 85%Mianzhao Wang →
“Noise-Robust Box-Supervised Infrared Small Target Detection via Physics-Inspired Soft Label Optimization”
- LinkedLinked via arxiv author · 85%Jiangpeng Zheng →
“Noise-Robust Box-Supervised Infrared Small Target Detection via Physics-Inspired Soft Label Optimization”
- LinkedLinked via arxiv author · 85%Xu Cheng →
“Noise-Robust Box-Supervised Infrared Small Target Detection via Physics-Inspired Soft Label Optimization”