Abstract: Transformer is a new kind of neural architecture which encodes the input dataas powerful features via the attention mechanism. Basically, the visualtransformers first divide the input images into several local patches and thencalculate both representations and their relationship. Since natural images areof high complexity with abundant detail and color information, the granularityof the patch dividing is not fine enough for excavating features of objects indifferent scales and locations. In this paper, we point out that the attentioninside these local patches are also essential for building visual transformerswith high performance and we explore a new architecture, namely, Transformer iNTransformer (TNT). Specifically, we regard the local patches (e.g.,16$times$16) as 'visual sentences' and present to further divide them intosmaller patches (e.g., 4$times$4) as 'visual words'. The attention of eachword will be calculated with other words in the given visual sentence withnegligible computational costs. Features of both words and sentences will beaggregated to enhance the representation ability. Experiments on severalbenchmarks demonstrate the effectiveness of the proposed TNT architecture,e.g., we achieve an $81.5%$ top-1 accuracy on the ImageNet, which is about$1.7%$ higher than that of the state-of-the-art visual transformer with similarcomputational cost. The PyTorch code is available atthis https URL, and theMindSpore code is atthis https URL.
Submission history
From: Kai Han [view email] [v1]
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Sat, 27 Feb 2021 03:12:16 UTC (4,472 KB)[v2]Mon, 5 Jul 2021 03:31:05 UTC (7,456 KB)
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References & Citations
DBLP - CS Bibliography
Jianyuan Guo
Chunjing Xu
Yunhe Wang
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