DuoFormer: Leveraging Hierarchical Representations by Local and Global Attention Vision Transformer
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916795025195008 |
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| author | Tang, Xiaoya Zhang, Bodong Ho, Man Minh Knudsen, Beatrice S. Tasdizen, Tolga |
| author_facet | Tang, Xiaoya Zhang, Bodong Ho, Man Minh Knudsen, Beatrice S. Tasdizen, Tolga |
| contents | Despite the widespread adoption of transformers in medical applications, the exploration of multi-scale learning through transformers remains limited, while hierarchical representations are considered advantageous for computer-aided medical diagnosis. We propose a novel hierarchical transformer model that adeptly integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the advanced representational potential of Vision Transformers (ViTs). Addressing the lack of inductive biases and dependence on extensive training datasets in ViTs, our model employs a CNN backbone to generate hierarchical visual representations. These representations are adapted for transformer input through an innovative patch tokenization process, preserving the inherited multi-scale inductive biases. We also introduce a scale-wise attention mechanism that directly captures intra-scale and inter-scale associations. This mechanism complements patch-wise attention by enhancing spatial understanding and preserving global perception, which we refer to as local and global attention, respectively. Our model significantly outperforms baseline models in terms of classification accuracy, demonstrating its efficiency in bridging the gap between Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). The components are designed as plug-and-play for different CNN architectures and can be adapted for multiple applications. The code is available at https://github.com/xiaoyatang/DuoFormer.git. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_12982 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DuoFormer: Leveraging Hierarchical Representations by Local and Global Attention Vision Transformer Tang, Xiaoya Zhang, Bodong Ho, Man Minh Knudsen, Beatrice S. Tasdizen, Tolga Computer Vision and Pattern Recognition Despite the widespread adoption of transformers in medical applications, the exploration of multi-scale learning through transformers remains limited, while hierarchical representations are considered advantageous for computer-aided medical diagnosis. We propose a novel hierarchical transformer model that adeptly integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the advanced representational potential of Vision Transformers (ViTs). Addressing the lack of inductive biases and dependence on extensive training datasets in ViTs, our model employs a CNN backbone to generate hierarchical visual representations. These representations are adapted for transformer input through an innovative patch tokenization process, preserving the inherited multi-scale inductive biases. We also introduce a scale-wise attention mechanism that directly captures intra-scale and inter-scale associations. This mechanism complements patch-wise attention by enhancing spatial understanding and preserving global perception, which we refer to as local and global attention, respectively. Our model significantly outperforms baseline models in terms of classification accuracy, demonstrating its efficiency in bridging the gap between Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). The components are designed as plug-and-play for different CNN architectures and can be adapted for multiple applications. The code is available at https://github.com/xiaoyatang/DuoFormer.git. |
| title | DuoFormer: Leveraging Hierarchical Representations by Local and Global Attention Vision Transformer |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.12982 |