Discover, Segment, and Select: A Progressive Mechanism for Zero-shot Camouflaged Object Segmentation
This work addresses the challenge of segmenting camouflaged objects in computer vision, offering an incremental improvement over existing two-stage methods.
The paper tackles the problem of inaccurate localization and detection in zero-shot camouflaged object segmentation by proposing a progressive Discover-Segment-Select (DSS) mechanism, which achieves state-of-the-art performance on multiple benchmarks without training or supervision.
Current zero-shot Camouflaged Object Segmentation methods typically employ a two-stage pipeline (discover-then-segment): using MLLMs to obtain visual prompts, followed by SAM segmentation. However, relying solely on MLLMs for camouflaged object discovery often leads to inaccurate localization, false positives, and missed detections. To address these issues, we propose the \textbf{D}iscover-\textbf{S}egment-\textbf{S}elect (\textbf{DSS}) mechanism, a progressive framework designed to refine segmentation step by step. The proposed method contains a Feature-coherent Object Discovery (FOD) module that leverages visual features to generate diverse object proposals, a segmentation module that refines these proposals through SAM segmentation, and a Semantic-driven Mask Selection (SMS) module that employs MLLMs to evaluate and select the optimal segmentation mask from multiple candidates. Without requiring any training or supervision, DSS achieves state-of-the-art performance on multiple COS benchmarks, especially in multiple-instance scenes.