Stepwise Decomposition and Dual-stream Focus: A Novel Approach for Training-free Camouflaged Object Segmentation (2506.06818v1)

Abstract: While promptable segmentation (\textit{e.g.}, SAM) has shown promise for various segmentation tasks, it still requires manual visual prompts for each object to be segmented. In contrast, task-generic promptable segmentation aims to reduce the need for such detailed prompts by employing only a task-generic prompt to guide segmentation across all test samples. However, when applied to Camouflaged Object Segmentation (COS), current methods still face two critical issues: 1) \textit{\textbf{semantic ambiguity in getting instance-specific text prompts}}, which arises from insufficient discriminative cues in holistic captions, leading to foreground-background confusion; 2) \textit{\textbf{semantic discrepancy combined with spatial separation in getting instance-specific visual prompts}}, which results from global background sampling far from object boundaries with low feature correlation, causing SAM to segment irrelevant regions. To address the issues above, we propose \textbf{RDVP-MSD}, a novel training-free test-time adaptation framework that synergizes \textbf{R}egion-constrained \textbf{D}ual-stream \textbf{V}isual \textbf{P}rompting (RDVP) via \textbf{M}ultimodal \textbf{S}tepwise \textbf{D}ecomposition Chain of Thought (MSD-CoT). MSD-CoT progressively disentangles image captions to eliminate semantic ambiguity, while RDVP injects spatial constraints into visual prompting and independently samples visual prompts for foreground and background points, effectively mitigating semantic discrepancy and spatial separation. Without requiring any training or supervision, RDVP-MSD achieves a state-of-the-art segmentation result on multiple COS benchmarks and delivers a faster inference speed than previous methods, demonstrating significantly improved accuracy and efficiency. The codes will be available at \href{https://github.com/ycyinchao/RDVP-MSD}{https://github.com/ycyinchao/RDVP-MSD}

• The paper introduces RDVP-MSD, which integrates multimodal stepwise decomposition and dual-stream visual prompting to advance camouflaged object segmentation.
• It tackles semantic ambiguity and spatial discrepancies, achieving up to 7.2% improvement in F-measure and a 19.0% reduction in mean absolute error on key benchmarks.
• The training-free framework offers efficient segmentation, promising significant applications in surveillance, wildlife monitoring, and autonomous navigation.

A Novel Approach for Training-free Camouflaged Object Segmentation

The research paper "Stepwise Decomposition and Dual-stream Focus: A Novel Approach for Training-free Camouflaged Object Segmentation" by Yin et al. introduces an innovative framework termed RDVP-MSD, aimed at improving the efficiency and accuracy of Camouflaged Object Segmentation (COS) without necessitating training or supervision. This work represents a significant advance in the field of COS, a task known for its complexity due to the high visual similarity between camouflaged objects and their backgrounds.

Core Challenges in Camouflaged Object Segmentation

The paper identifies two primary challenges in COS: 1) semantic ambiguity in generating instance-specific text prompts, which results in foreground-background confusion, and 2) semantic discrepancy and spatial separation in generating instance-specific visual prompts. These challenges impact the Segmentation Anything Model (SAM) by causing it to segment irrelevant regions, thereby reducing segmentation precision.

Proposed Solution: RDVP-MSD Framework

The core of the proposed solution lies in the RDVP-MSD framework, which integrates a Region-constrained Dual-stream Visual Prompting (RDVP) mechanism with a Multimodal Stepwise Decomposition Chain of Thought (MSD-CoT) methodology.

Multimodal Stepwise Decomposition Chain of Thought (MSD-CoT): This component addresses semantic ambiguity by progressively disentangling image captions into phrase-level and word-level instance-specific text prompts. This structured decomposition reduces misclassification, thereby enhancing the accuracy of text prompts used for segmentation.

Region-constrained Dual-stream Visual Prompting (RDVP): This strategy introduces spatial constraints into visual prompting by independently sampling visual prompts for foreground and background points within object bounding boxes. RDVP mitigates semantic discrepancy and spatial separation, focusing on regions that are most likely to contain the camouflaged object.

Experimental Validation

The efficacy of RDVP-MSD is validated across multiple standard benchmarks for COS, achieving state-of-the-art performance without any training or supervisory data. The framework significantly outperforms both manually supervised and existing task-generic promptable segmentation methods, achieving higher segmentation accuracy and efficiency. For instance, RDVP-MSD surpasses prior methods such as GenSAM and ProMaC by 6.8% in structure measure ($S_{\alpha}$), 7.2% in F-measure ($F_{\beta}$), and reduces the mean absolute error ($M$) by 19.0% on the CAMO dataset.

Implications and Future Directions

This work has substantial implications for COS applications, including military surveillance, wildlife monitoring, and autonomous navigation, where manual annotation is impractical. The training-free nature of RDVP-MSD suggests it could be extended to other segmentation tasks involving high visual similarity between objects of interest and their surroundings.

Future research could explore the integration of RDVP-MSD with other foundation models to further enhance segmentation capabilities. Additionally, further refinement in the MLLM capabilities for generating more precise instance-specific prompts could offer even greater accuracy.

In essence, the framework introduced by Yin et al. holds promise for advancing COS without reliance on cumbersome datasets, marking a pivotal shift towards more adaptable and efficient segmentation technologies.