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- W4285606711 abstract "Despite the success of vision Transformers for the image deraining task, they are limited by computation-heavy and slow runtime. In this work, we investigate Transformer decoder is not necessary and has huge computational costs. Therefore, we revisit the standard vision Transformer as well as its successful variants and propose a novel Decoder-Free Transformer-Like (DFTL) architecture for fast and accurate single image deraining. Specifically, we adopt a cheap linear projection to represent visual information with lower computational costs than previous linear projections. Then we replace standard Transformer decoder block with designed Progressive Patch Merging (PPM), which attains comparable performance and efficiency. DFTL could significantly alleviate the computation and GPU memory requirements through proposed modules. Extensive experiments demonstrate the superiority of DFTL compared with competitive Transformer architectures, e.g., ViT, DETR, IPT, Uformer, and Restormer. The code is available at https://github.com/XiaoXiao-Woo/derain." @default.
- W4285606711 created "2022-07-16" @default.
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- W4285606711 date "2022-07-01" @default.
- W4285606711 modified "2023-10-16" @default.
- W4285606711 title "A Decoder-free Transformer-like Architecture for High-efficiency Single Image Deraining" @default.
- W4285606711 doi "https://doi.org/10.24963/ijcai.2022/202" @default.
- W4285606711 hasPublicationYear "2022" @default.
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