SPARK-SAM: Learning How to Prompt and Respond for Infrared Small Target Segmentation
Promptable segmentation models provide a reusable interface, but direct transfer to automatic infrared small-target segmentation (IRSTD) exposes a mismatch between spatial prompts and target-domain mask responses. In a diagnostic using target-covering loose-box prompts deterministically derived from test reference masks, the best official SAM2.1 results are only 4.69%, 1.64%, and 2.28% IoU on NUAA-SIRST, NUDT-SIRST, and IRSTD-1K. We introduce SPARK-SAM (Self-Prompt Adaptation with Response Knowledge for SAM), which learns target-domain response knowledge and conditions the decoder through an image-conditioned joint self-prompt state. Training combines benchmark-mask supervision with relia...
| Authors | Aji Mao, Zhenming Peng, Bailin Mu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 21 August 2026 |
| Last updated at source | 26 August 2026 |
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