Skip to yearly menu bar Skip to main content


Language-guided Image Reflection Separation

Haofeng Zhong · Yuchen Hong · Shuchen Weng · Jinxiu Liang · Boxin Shi

Arch 4A-E Poster #68
[ ]
Fri 21 Jun 5 p.m. PDT — 6:30 p.m. PDT


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.

Live content is unavailable. Log in and register to view live content