This paper studies the problem of language-guided reflection separation, which aims at addressing the ill-posed reflection separation problem by introducing language descriptions to provide layer content. We propose a unified framework to solve this problem, which leverages the cross-attention mechanism with contrastive learning strategies to construct the correspondence between language descriptions and image layers. A gated network design and a randomized training strategy are employed to tackle the recognizable layer ambiguity. The effectiveness of the proposed method is validated by the significant performance advantage over existing reflection separation methods on both quantitative and qualitative comparisons.

翻译：本文研究语言引导的反射分离问题，旨在通过引入语言描述提供图层内容来解决不适定的反射分离问题。我们提出了一个统一框架，利用跨注意力机制与对比学习策略构建语言描述与图像层之间的对应关系。采用门控网络设计与随机化训练策略来处理可识别层歧义问题。通过定量与定性比较，实验结果表明所提方法在性能上显著优于现有反射分离方法。