Language-guided Image Reflection Separation
CVPR 2024(2024)
摘要
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.
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