🤖 AI Summary
This work addresses the challenge of amodal instance segmentation in occluded regions, where the absence of pixel observations necessitates reliance on shape priors. The authors propose a reliability-adaptive shape prior framework that dynamically composes instance-specific priors through cross-attention over learnable shape prototypes. To modulate the influence of these priors according to occlusion severity, the method employs the signed distance field of the visible mask as a spatial gating signal, adaptively controlling the strength of prior injection. This approach avoids both uniform prior application and complex generative models, achieving state-of-the-art performance on two standard amodal instance segmentation benchmarks. Under standard evaluation settings, it improves mIoU in occluded regions by over 11 percentage points while using only about one-third the parameters of previous methods.
📝 Abstract
Amodal instance segmentation aims to predict the complete object mask including occluded regions that lack pixel-level observations and must be inferred with the aid of shape priors. Existing methods acquire shape priors through fixed-capacity encoding spaces or expensive generative models, and inject them uniformly across all spatial positions without adapting to the varying prior demand between visible and occluded regions. In this paper, we propose a gated reliability-adaptive shape prior framework, which introduces a shape prior memory module that combines learnable prototypes via cross-attention to produce instance-adaptive shape priors through weighted prototype combination rather than generation. A spatial adaptive reliability gate then employs the signed distance field of the visible mask to modulate injection intensity at each position according to its occlusion depth, preserving reliable features in visible regions while directing shape compensation toward occluded areas. Experiments on two mainstream amodal instance segmentation benchmarks demonstrate that the proposed method outperforms existing approaches under multiple evaluation settings, improving the mean intersection-over-union over occluded regions by over 11 percentage points on one of the two benchmarks under the standard setting, while using approximately one-third of the total parameters. Linear probing analysis further reveals that the visible-mask cross-attention module implicitly encodes occlusion geometry into visual token representations, explaining the effectiveness of the proposed module decomposition.