Before revision:  In this paper, we propose a novel Transformer-iN-Transformer (TNT) model for modeling both patch-level and pixel-level representation. In each TNT block, an outer transformer block is utilized to process patch embeddings, and an inner transformer block extracts local features from pixel embeddings.The pixel-level feature is projected to the space of patch embedding by a linear transformation layer and then added into the patch. By stacking the TNT blocks, we build the TNT model for image recognition. Experiments on ImageNet benchmark and downstream tasks demonstrate the superiority and efficiency\times16\times4 of the proposed TNT architecture .
After revision:  In this paper, we propose a novel Transformer-iN-Transformer (TNT) . Specifically, we regard the local patches (e.g., 16\times16\times4 of the proposed TNT architecture .