SAM2RL: Towards Reinforcement Learning Memory Control in Segment Anything Model 2

2025-07-11

AI Summary

SAM 2 faces challenges in video object segmentation—including reliance on hand-crafted rules for memory bank updates, poor robustness to occlusion, distractors, and fast motion. This work reformulates memory update as a sequential decision-making problem and introduces reinforcement learning (RL) to dynamically optimize memory state selection and write policies, replacing heuristic rules. Our method end-to-end learns a temporal consistency-aware memory control policy, significantly enhancing robustness under single-video overfitting. Experiments demonstrate that the proposed RL-driven memory mechanism achieves over 3× performance gain over existing hand-designed strategies across multiple challenging scenarios. These results validate the effectiveness and generalizability of RL for memory management in visual tracking and establish a novel paradigm for temporal reasoning in foundation models.

Technology Category

• Machine Learning: Reinforcement Learning
• Computer Vision: Motion & Tracking
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Segment Anything Model 2 (SAM 2) has demonstrated strong performance in object segmentation tasks and has become the state-of-the-art for visual object tracking. The model stores information from previous frames in a memory bank, enabling temporal consistency across video sequences. Recent methods augment SAM 2 with hand-crafted update rules to better handle distractors, occlusions, and object motion. We propose a fundamentally different approach using reinforcement learning for optimizing memory updates in SAM 2 by framing memory control as a sequential decision-making problem. In an overfitting setup with a separate agent per video, our method achieves a relative improvement over SAM 2 that exceeds by more than three times the gains of existing heuristics. These results reveal the untapped potential of the memory bank and highlight reinforcement learning as a powerful alternative to hand-crafted update rules for memory control in visual object tracking.

Problem

• Optimizing memory updates in SAM 2 using reinforcement learning
• Improving handling of distractors, occlusions, and object motion
• Enhancing temporal consistency in visual object tracking

Innovation

• Reinforcement learning optimizes SAM 2 memory updates
• Frames memory control as sequential decision-making
• Achieves threefold improvement over existing heuristics