Diffuse to Detect: Bi-Level Sample Rebalancing with Pseudo-Label Diffusion for Point-Supervised Infrared Small-Target Detection
Researchers have developed a new framework for infrared small-target detection using point supervision, addressing challenges of unstable pseudo-labels and sample imbalance. Their approach utilizes a physics-induced annotation strategy based on heat diffusion to generate reliable pseudo-masks from single-point labels. A bi-level dual-update framework optimizes detector weights, sample weights, and diffusion parameters, enhancing supervision and adapting to sample distribution. AI
IMPACT Introduces a novel method for improving the accuracy and efficiency of infrared small-target detection using physics-informed AI.