HFSTI-Net：视频息肉分割的层级频率-空间-时间交互

ICLR 2026 Poster accepted paper at ICLR 2026. Automatic video polyp segmentation (VPS) is crucial for preventing and treating colorectal cancer by ensuring accurate identification of polyps in colonoscopy examinations. However, its clinical application is hampered by two key challenges: shape collapse, which compromises structural integrity, and episodic amnesia, which causes instability in challenging video sequences. To address these challenges, we present a novel video segmentation network, \emph{HFSTI-Net}, which integrates global perception with spatiotemporal consistency in spatial, temporal, and frequency domains. Specifically, to address shape collapse under low contrast or visual ambiguity, we design a Hierarchical Frequency-spatial Interaction (HFSI) module that fuses spatial and frequency cues for fine-grained boundary localization. Code/project link: https://github.com/Yuanqin-He/HFSTI-Net