Co-Scale Conv-Attentional Image Transformers

• URL: http://arxiv.org/abs/2104.06399v1
• Date: Tue, 13 Apr 2021 17:58:29 GMT
• Title: Co-Scale Conv-Attentional Image Transformers
• Authors: Weijian Xu, Yifan Xu, Tyler Chang, Zhuowen Tu
• Abstract summary: Co-scale conv-attentional image Transformers (CoaT) are a Transformer-based image classifier equipped with co-scale and conv-attentional mechanisms. On ImageNet, relatively small CoaT models attain superior classification results compared with the similar-sized convolutional neural networks and image/vision Transformers.
• Score: 22.834316796018705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present Co-scale conv-attentional image Transformers (CoaT), a Transformer-based image classifier equipped with co-scale and conv-attentional mechanisms. First, the co-scale mechanism maintains the integrity of Transformers' encoder branches at individual scales, while allowing representations learned at different scales to effectively communicate with each other; we design a series of serial and parallel blocks to realize the co-scale attention mechanism. Second, we devise a conv-attentional mechanism by realizing a relative position embedding formulation in the factorized attention module with an efficient convolution-like implementation. CoaT empowers image Transformers with enriched multi-scale and contextual modeling capabilities. On ImageNet, relatively small CoaT models attain superior classification results compared with the similar-sized convolutional neural networks and image/vision Transformers. The effectiveness of CoaT's backbone is also illustrated on object detection and instance segmentation, demonstrating its applicability to the downstream computer vision tasks.

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