![]() Specifically, we regard the local patches (e.g.,ġ6$\times$16) as "visual sentences" and present to further divide them into With high performance and we explore a new architecture, namely, Transformer iN Inside these local patches are also essential for building visual transformers In this paper, we point out that the attention Of the patch dividing is not fine enough for excavating features of objects inĭifferent scales and locations. Of high complexity with abundant detail and color information, the granularity ![]() Transformers first divide the input images into several local patches and thenĬalculate both representations and their relationship. ![]() ![]() Download a PDF of the paper titled Transformer in Transformer, by Kai Han and 5 other authors Download PDF Abstract: Transformer is a new kind of neural architecture which encodes the input dataĪs powerful features via the attention mechanism. ![]()
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