Rethinking IRSTD: Single-Point Supervision Guided Encoder-only Framework is Enough for Infrared Small Target Detection

摘要

Infrared small target detection (IRSTD) aims to localizesmall targets in cluttered infrared scenes. Extensive research follows thepixel-level supervision-guided ‘encoder–decoder’ segmentation paradigm.Although these methods have achieved promising performance, they of-ten overlook that infrared small targets occupy only a few pixels andare usually surrounded by blurred, low-contrast boundaries caused bycluttered backgrounds. Based on this observation, we argue that thefirst principle of IRSTD should be target localization rather than densereconstruction of target regions entangled with indistinguishable back-ground noise. In this paper, we reformulate IRSTD as a centroid re-gression task and propose a novel Single-Point Supervision guided In-frared Probabilistic Response Encoding method (namely, SPIRE), whichis non-trivial because point-level supervision must produce detectionoutputs comparable to dense supervision. Specifically, we design Point-Response Prior Supervision (PRPS) to transform single-point annota-tions into probabilistic response maps consistent with infrared point-target response characteristics, and combine it with a High-ResolutionProbabilistic Encoder (HRPE) that performs encoder-only probabilis-tic regression followed by lightweight peak-based inference, without de-coder reconstruction. By preserving high-resolution features and increas-ing effective supervision density, SPIRE alleviates optimization instabil-ity under sparse target distributions. Finally, extensive experiments ontwo public IRSTD benchmarks, SIRST-UAVB and SIRST4, demonstratethat SPIRE achieves competitive target-level detection performance withconsistently low false alarm rate (Fa) and significantly reduced compu-tational cost.