CoT-Saliency: Unified Chain-of-Thought Reasoning for Heterogeneous Saliency Tasks

• URL: http://arxiv.org/abs/2511.00396v1
• Date: Sat, 01 Nov 2025 04:37:01 GMT
• Title: CoT-Saliency: Unified Chain-of-Thought Reasoning for Heterogeneous Saliency Tasks
• Authors: Long Li, Shuichen Ji, Ziyang Luo, Nian Liu, Dingwen Zhang, Junwei Han,
• Abstract summary: We present the first unified framework that jointly handles three operationally heterogeneous saliency tasks.<n>We introduce a Chain-of-Thought (CoT) reasoning process in a Vision-Language Model (VLM) to bridge task heterogeneity.<n>We show our model matches or outperforms specialized SOTA methods and strong closed-source VLMs across all tasks.
• Score: 96.64597365827046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present the first unified framework that jointly handles three operationally heterogeneous saliency tasks, eg, SOD, CoSOD, and SIS, by casting each as a Chain-of-Thought (CoT) reasoning process in a Vision-Language Model (VLM) to bridge task heterogeneity. CoT training follows a two-stage paradigm: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). To enhance CoT quality in RL, we propose Confidence-Guided Policy Optimization (CGPO), a lightweight single-sample algorithm that leverages the discrepancy between reward and model confidence as a per-sample advantage signal. This design naturally focuses updates on informative responses while eliminating group sampling, thereby addressing GRPO's key limitations: confidence-agnostic learning, signal dilution, and prohibitive computational overhead. We also introduce an "output-to-reasoning" strategy to construct high-fidelity SFT data that ensures logical consistency with ground-truth masks. Experiments show our model matches or outperforms specialized SOTA methods and strong closed-source VLMs across all tasks, especially achieving an S-measure of 0.899 on CoCA for CoSOD, surpassing the prior best by 8.0 percentage points, despite using far less training data.

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