{"type":"ScholarlyArticle","title":"Learning with Semantic Priors: Stabilizing Point-Supervised Infrared Small Target Detection via Hierarchical Knowledge Distillation","authors":[{"name":"Yuanhang Yao","slug":"yuanhang-yao","given":"Yuanhang","family":"Yao","affiliation":""},{"name":"Ping Qian","slug":"ping-qian","given":"Ping","family":"Qian","affiliation":""},{"name":"Zhu Liu","slug":"zhu-liu","given":"Zhu","family":"Liu","affiliation":""},{"name":"Long Ma","slug":"long-ma","given":"Long","family":"Ma","affiliation":""},{"name":"Weimin Wang","slug":"weimin-wang","given":"Weimin","family":"Wang","affiliation":""}],"abstract":"Single-frame Infrared Small Target Detection (ISTD) aims to localize weak targets under heavy background clutter, yet dense pixel-wise annotations are expensive. Point supervision with online label evolution reduces annotation cost; however, lightweight CNN detectors often lack sufficient semantics, leading to noisy pseudo-masks and unstable optimization. To address this, we propose a hierarchical VFM-driven knowledge distillation framework that uses a frozen Vision Foundation Model (VFM) during training. We formulate point-supervised learning as a bilevel optimization process: the inner loop adapts a VFM-embedded teacher on reweighted training samples, while the outer loop transfers validation-guided knowledge to a lightweight student to mitigate pseudo-label noise and training-set bias. We further introduce Semantic-Conditioned Affine Modulation (SCAM) to inject VFM semantics into CNN features at multiple layers. In addition, a dynamic collaborative learning strategy with cluster-level sample reweighting enhances robustness to imperfect pseudo-masks. Experiments on diverse challenging cases across multiple ISTD backbones demonstrate consistent improvements in detection accuracy and training stability. Our code is available at https://github.com/yuanhang-yao/semantic-prior.","doi":"10.24963/ijcai.2026/224","url":"https://ijcai.org/proceedings/2026/224","pdf":"https://ijcai.org/proceedings/2026/0224.pdf","markdown":"https://ijcai.org/proceedings/2026/224.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/224","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Computer Vision","track":"Main Track","number":224,"pages":{"first":2010,"last":2018},"keywords":["Computer Vision: Transfer, low-shot, semi- and un- supervised learning","Computer Vision: Segmentation, grouping and shape analysis"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}