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- W4387486536 abstract "In this paper, we present a novel transformer-based architecture for end-to-end image compression. Our architecture incorporates blocks that effectively capture local dependencies between tokens, eliminating the need for positional encoding by integrating convolutional operations within the multi-head attention mechanism. We demonstrate through experiments that our proposed framework surpasses state-of-the-art CNN-based architectures in terms of the trade-off between bit-rate and distortion and achieves comparable results to transformer-based methods while maintaining lower computational complexity." @default.
- W4387486536 created "2023-10-11" @default.
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- W4387486536 date "2023-09-20" @default.
- W4387486536 modified "2023-10-16" @default.
- W4387486536 title "Convolutional Transformer-Based Image Compression" @default.
- W4387486536 cites W2601564443 @default.
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- W4387486536 doi "https://doi.org/10.23919/spa59660.2023.10274433" @default.
- W4387486536 hasPublicationYear "2023" @default.
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