MAFormer: A Transformer Network with Multi-scale Attention Fusion for Visual Recognition

• URL: http://arxiv.org/abs/2209.01620v1
• Date: Wed, 31 Aug 2022 06:29:27 GMT
• Title: MAFormer: A Transformer Network with Multi-scale Attention Fusion for Visual Recognition
• Authors: Yunhao Wang, Huixin Sun, Xiaodi Wang, Bin Zhang, Chao Li, Ying Xin, Baochang Zhang, Errui Ding, Shumin Han
• Abstract summary: We introduce Multi-scale Attention Fusion into transformer (MAFormer) MAFormer explores local aggregation and global feature extraction in a dual-stream framework for visual recognition. Our MAFormer achieves state-of-the-art performance on common vision tasks.
• Score: 45.68567088645708
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision Transformer and its variants have demonstrated great potential in various computer vision tasks. But conventional vision transformers often focus on global dependency at a coarse level, which suffer from a learning challenge on global relationships and fine-grained representation at a token level. In this paper, we introduce Multi-scale Attention Fusion into transformer (MAFormer), which explores local aggregation and global feature extraction in a dual-stream framework for visual recognition. We develop a simple but effective module to explore the full potential of transformers for visual representation by learning fine-grained and coarse-grained features at a token level and dynamically fusing them. Our Multi-scale Attention Fusion (MAF) block consists of: i) a local window attention branch that learns short-range interactions within windows, aggregating fine-grained local features; ii) global feature extraction through a novel Global Learning with Down-sampling (GLD) operation to efficiently capture long-range context information within the whole image; iii) a fusion module that self-explores the integration of both features via attention. Our MAFormer achieves state-of-the-art performance on common vision tasks. In particular, MAFormer-L achieves 85.9$\%$ Top-1 accuracy on ImageNet, surpassing CSWin-B and LV-ViT-L by 1.7$\%$ and 0.6$\%$ respectively. On MSCOCO, MAFormer outperforms the prior art CSWin by 1.7$\%$ mAPs on object detection and 1.4$\%$ on instance segmentation with similar-sized parameters, demonstrating the potential to be a general backbone network.

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