Can I read ShadowMaskFormer: Mask Augmented Patch Embeddings for Shadow Removal on EtoBox?

ShadowMaskFormer: Mask Augmented Patch Embeddings for Shadow Removal by Li, Zhuohao; Xie, Guoyang; Jiang, Guannan; Lu, Zhichao is a scholarly article available to read on EtoBox.

What is ShadowMaskFormer: Mask Augmented Patch Embeddings for Shadow Removal about?

Transformer recently emerged as the de facto model for computer vision tasks and has also been successfully applied to shadow removal. However, these existing methods heavily rely on intricate modifications to the attention mechanisms within the transformer blocks while using a generic patch embedding. As a result, it often leads to complex architectural designs requiring additional computation resources. In this work, we aim to explore the efficacy of incorporating shadow information within the early processing stage. Accordingly, we propose a transformer-based framework with a novel patch embedding that is tailored for shadow removal, dubbed ShadowMaskFormer. Specifically, we present a simple and effective mask-augmented patch embedding to integrate shadow information and promote the model's emphasis on acquiring knowledge for shadow regions. Extensive experiments conducted on the ISTD, ISTD+, and SRD benchmark datasets demonstrate the efficacy of our method against state-of-the-art approaches while using fewer model parameters.g fewer model parameters. Our implementation is available at https://github.com/lizhh268/ShadowMaskFormer.

Author

Li, Zhuohao; Xie, Guoyang; Jiang, Guannan; Lu, Zhichao

Published

2024

Language

EN