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      Laplacian-Former: Overcoming the Limitations of Vision Transformers in Local Texture Detection

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          Abstract

          Vision Transformer (ViT) models have demonstrated a breakthrough in a wide range of computer vision tasks. However, compared to the Convolutional Neural Network (CNN) models, it has been observed that the ViT models struggle to capture high-frequency components of images, which can limit their ability to detect local textures and edge information. As abnormalities in human tissue, such as tumors and lesions, may greatly vary in structure, texture, and shape, high-frequency information such as texture is crucial for effective semantic segmentation tasks. To address this limitation in ViT models, we propose a new technique, Laplacian-Former, that enhances the self-attention map by adaptively re-calibrating the frequency information in a Laplacian pyramid. More specifically, our proposed method utilizes a dual attention mechanism via efficient attention and frequency attention while the efficient attention mechanism reduces the complexity of self-attention to linear while producing the same output, selectively intensifying the contribution of shape and texture features. Furthermore, we introduce a novel efficient enhancement multi-scale bridge that effectively transfers spatial information from the encoder to the decoder while preserving the fundamental features. We demonstrate the efficacy of Laplacian-former on multi-organ and skin lesion segmentation tasks with +1.87\% and +0.76\% dice scores compared to SOTA approaches, respectively. Our implementation is publically available at https://github.com/mindflow-institue/Laplacian-Former

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          Author and article information

          Journal
          31 August 2023
          Article
          2309.00108
          a36644cf-256d-424a-a7be-fce62b95cc07

          http://creativecommons.org/licenses/by/4.0/

          History
          Custom metadata
          Accepted in the main conference MICCAI 2023
          cs.CV

          Computer vision & Pattern recognition
          Computer vision & Pattern recognition

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