Couplformer:Rethinking Vision Transformer with Coupling Attention Map

• URL: http://arxiv.org/abs/2112.05425v1
• Date: Fri, 10 Dec 2021 10:05:35 GMT
• Title: Couplformer:Rethinking Vision Transformer with Coupling Attention Map
• Authors: Hai Lan, Xihao Wang, Xian Wei
• Abstract summary: The Transformer model has demonstrated its outstanding performance in the computer vision domain. We propose a novel memory economy attention mechanism named Couplformer, which decouples the attention map into two sub-matrices. Experiments show that the Couplformer can significantly decrease 28% memory consumption compared with regular Transformer.
• Score: 7.789667260916264
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: With the development of the self-attention mechanism, the Transformer model has demonstrated its outstanding performance in the computer vision domain. However, the massive computation brought from the full attention mechanism became a heavy burden for memory consumption. Sequentially, the limitation of memory reduces the possibility of improving the Transformer model. To remedy this problem, we propose a novel memory economy attention mechanism named Couplformer, which decouples the attention map into two sub-matrices and generates the alignment scores from spatial information. A series of different scale image classification tasks are applied to evaluate the effectiveness of our model. The result of experiments shows that on the ImageNet-1k classification task, the Couplformer can significantly decrease 28% memory consumption compared with regular Transformer while accessing sufficient accuracy requirements and outperforming 0.92% on Top-1 accuracy while occupying the same memory footprint. As a result, the Couplformer can serve as an efficient backbone in visual tasks, and provide a novel perspective on the attention mechanism for researchers.

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