Prompt and Refinement: Asymmetric Mutual Learning for Infrared Small Target Detection with Noisy Labels
Existing data-driven infrared small target detection (ISTD) methods typically require large-scale datasets with accurate pixel-level annotations for model training. However, such labor-intensive requirements are difficult to satisfy in real-world applications due to the heavy reliance on expert knowledge and the inherently weak distinctiveness of infrared small targets. Consequently, the presence of noisy labels during model training is inevitable, which can severely mislead the learning of target perception toward spurious patterns. To address this challenge, we propose Prompt and Refinement (PAR), a label-noise-robust asymmetric mutual learning paradigm for ISTD. Specifically, PAR compr...
| Authors | Yimin Fu, Songbo Wang, Lizhuo Liu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 5 October 2026 |
| Last updated at source | 5 October 2026 |
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