  Transformer has been applied in the field of computer vision due to its
excellent performance in natural language processing, surpassing traditional
convolutional neural networks and achieving new state-of-the-art. ViT divides
an image into several local patches, known as "visual sentences". However, the
information contained in the image is vast and complex, and focusing only on
the features at the "visual sentence" level is not enough. The features between
local patches should also be taken into consideration. In order to achieve
further improvement, the TNT model is proposed, whose algorithm further divides
the image into smaller patches, namely "visual words," achieving more accurate
results. The core of Transformer is the Multi-Head Attention mechanism, and
traditional attention mechanisms ignore interactions across different attention
heads. In order to reduce redundancy and improve utilization, we introduce the
nested algorithm and apply the Nested-TNT to image classification tasks. The
experiment confirms that the proposed model has achieved better classification
performance over ViT and TNT, exceeding 2.25%, 1.1% on dataset CIFAR10 and
2.78%, 0.25% on dataset FLOWERS102 respectively.
