HIDA: A Human-Intuition-Guided Depth-Aware Framework for Zero-Shot Amodal Segmentation

Amodal segmentation aims to recover the full shape of objects that are partially occluded; however, most existing approaches are trained and assessed on in-domain benchmarks and exhibit limited generalization in zero-shot scenarios. Human-intuition-guided depth-aware (HIDA) is proposed as a plug-and-play framework that combines two frozen vision foundation models with a lightweight trainable segmentation network to estimate amodal masks using only a visible bounding box. HIDA initially derives a visible prior mask and a pseudo-depth map (PDM) to enable target-centered cropping, which reduces irrelevant context while highlighting occlusion boundaries and hidden regions. An occlusion-aware token-routing soft MoE module is also introduced within a lightweight ViT encoder to adaptively handle varied occlusion patterns and strengthen representations of invisible regions. Evaluations on COCOA-cls, KINS, and D2SA demonstrate that HIDA attains state-of-the-art performance in fully supervised settings and state-of-the-art zero-shot results on COCOA-cls and D2SA while training only a lightweight segmentation network. Code is available at: https://github.com/lipenghuai/HIDA.