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.

本文研究了语言引导的反射分离问题，通过引入语言描述以提供层次内容，解决了反射分离问题。我们提出了一个统一的框架来解决这个问题，该框架利用交叉注意力机制和对比学习策略构建语言描述和图像层之间的对应关系。采用门控网络设计和随机训练策略来处理可识别层的模糊性。基于定性和定量比较验证了所提方法相对于现有的反射分离方法的显著性能优势。