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

large-language-models › reasoning

📄 Abstract

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.

Authors (6)

Long Li

Shuichen Ji

Ziyang Luo

Nian Liu

Dingwen Zhang

Junwei Han

Submitted

November 1, 2025

arXiv Category

cs.CV

arXiv PDF

Key Contributions

CoT-Saliency introduces a unified framework for heterogeneous saliency tasks (SOD, CoSOD, SIS) by casting them as Chain-of-Thought (CoT) reasoning in VLMs. It proposes Confidence-Guided Policy Optimization (CGPO) for enhancing RL quality, which is more efficient than GRPO. An 'output-to-reasoning' strategy ensures logical consistency in SFT data, leading to high-fidelity reasoning and improved performance across diverse saliency tasks.

Business Value

Enables more robust and interpretable visual understanding systems, particularly for tasks requiring complex reasoning about image content, which can benefit applications in autonomous systems, content analysis, and robotics.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

Moderate. Requires sophisticated VLMs and RL training pipelines, which can be computationally intensive.

Limitations Addressed

Task heterogeneity in saliency prediction,Low quality of CoT reasoning in RL settings,Limitations of existing RL policy optimization (e.g., GRPO's confidence-agnostic learning, signal dilution, computational overhead)

Technical Tags

Chain-of-Thought (CoT)Vision-Language Models (VLMs)Saliency TasksHeterogeneous TasksSupervised Fine-Tuning (SFT)Reinforcement Learning (RL)Confidence-Guided Policy Optimization (CGPO)Mask GenerationLogical ConsistencyOutput-to-Reasoning

Research Topics

ReasoningVision-Language ModelsMulti-modal AIReinforcement LearningExplainable AI

Methods & Architectures

Chain-of-Thought (CoT) ReasoningSupervised Fine-Tuning (SFT)Reinforcement Learning (RL)Confidence-Guided Policy Optimization (CGPO)Output-to-Reasoning strategy Vision-Language Models (VLMs)

Applications & Tasks

Computer Vision Image Understanding Robotics AI Explainability Unifying heterogeneous saliency tasksImproving CoT reasoning quality in RLAddressing limitations of existing RL policy optimization methods Salient Object Detection (SOD)Camouflaged Object Detection (CoSOD)Image Segmentation (SIS)

Related Fields

Large Language ModelsComputer VisionReinforcement LearningMulti-modal AIExplainable AI

Keywords

Chain-of-ThoughtVision-Language ModelsSaliency DetectionReinforcement LearningReasoningMulti-modal AIExplainable AIComputer VisionPolicy OptimizationImage Segmentation

Academic Context

#Reasoning#Vision-Language Models#Multi-modal AI#Reinforcement Learning#Explainable AI

Commercial Potential

Potential Products

Advanced image analysis toolsRobotic vision systemsAI explainability platforms

Target Industries

TechnologyRoboticsAutonomous SystemsMedia

Use Case Examples

Precisely identifying salient objects in complex scenes for autonomous navigationGenerating explanations for why certain regions are considered important in an imageDeveloping robots that can reason about object properties and relationships

Competitive Edge

Provides a unified and more efficient framework for complex visual reasoning tasks compared to separate models or less efficient RL optimization methods.

Market Opportunity

Growing market for AI systems capable of complex reasoning and explanation.

Revenue Models

Licensing of advanced AI modelsintegration into specialized AI platforms.

Resource Requirements

Compute Needs

High (requires VLM training and RL optimization).

Data Requirements

Datasets for SOD, CoSOD, and SIS tasks, potentially requiring specialized data for CoT reasoning.

Deployment Constraints

Computational cost of VLM inference and RL policies,Complexity of training and fine-tuning

Scalability

Scalability depends on the underlying VLM and the efficiency of the CGPO algorithm.

Production Readiness

Maturity Level

Research

Time to Market

3-5 years

Patent Potential

Moderate, for the CGPO algorithm and the unified CoT framework for saliency.

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