更快更优：面向红外-可见光图像融合的强化协同蒸馏与自学习方法
Faster and Better: Reinforced Collaborative Distillation and Self-Learning for Infrared-Visible Image Fusion
AI总结：
针对轻量级模型难以实现高质量红外-可见光图像融合的问题，提出强化学习驱动的协同蒸馏与自学习框架，通过智能体生成难样本、动态调整教师指导强度，显著提升了学生模型性能与融合效果。
摘要：
红外与可见光图像融合通过结合不同模态的互补信息，在提升场景感知能力方面发挥着关键作用。尽管近年来取得了诸多进展，但利用轻量级模型实现高质量图像融合仍是一项重大挑战。为填补这一空白，我们提出了一种由强化学习驱动的新型图像融合协同蒸馏与自学习框架。与传统蒸馏不同，该方法不仅能让学生模型从教师模型中吸收图像融合知识，更重要的是，允许学生在更具挑战性的样本上进行自学习以增强自身能力。具体而言，在我们的框架中，一个强化学习智能体探索并确定更适合学生的训练策略。该智能体将学生的性能以及师生性能差距作为输入，生成具有挑战性的样本以促进学生的自学习。同时，它根据学生的状态动态调整教师的指导强度，以优化知识迁移。实验结果表明，与现有技术相比，我们的方法能够显著提升学生模型的性能，并取得更优的融合效果。
英文摘要：
Infrared and visible image fusion plays a critical role in enhancing scene perception by combining complementary information from different modalities. Despite recent advances, achieving high-quality image fusion with lightweight models remains a significant challenge. To bridge this gap, we propose a novel collaborative distillation and self-learning framework for image fusion driven by reinforcement learning. Unlike conventional distillation, this approach not only enables the student model to absorb image fusion knowledge from the teacher model, but more importantly, allows the student to perform self-learning on more challenging samples to enhance its capabilities. Particularly, in our framework, a reinforcement learning agent explores and identifies a more suitable training strategy for the student. The agent takes both the student's performance and the teacher-student gap as inputs, which leads to the generation of challenging samples to facilitate the student's self-learning. Simultaneously, it dynamically adjusts the teacher's guidance strength based on the student's state to optimize the knowledge transfer. Experimental results demonstrate that our method can significantly improve student performance and achieve better fusion results compared to existing techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
