# Nested-TNT: Hierarchical Vision Transformers with Multi-Scale Feature Processing

arXiv: https://arxiv.org/abs/2404.13434 · HTML: https://mlsanity.com/paper/2404.13434/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 17 references that are in the map.
- First posted on arXiv 20 Apr 2024.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [CvT: Introducing Convolutions to Vision Transformers](https://mlsanity.com/paper/2103.15808/) (2021)
- [Multiscale Vision Transformers](https://mlsanity.com/paper/2104.11227/) (2021)
- [Segmenter: Transformer for Semantic Segmentation](https://mlsanity.com/paper/2105.05633/) (2021)
- [Transformer in Transformer](https://mlsanity.com/paper/2103.00112/) (2021)
- [MViTv2: Improved Multiscale Vision Transformers for Classification and Detection](https://mlsanity.com/paper/2112.01526/) (2021)
- [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference](https://mlsanity.com/paper/2104.01136/) (2021)
- [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://mlsanity.com/paper/2106.00666/) (2021)
- [Part-based Face Recognition with Vision Transformers](https://mlsanity.com/paper/2212.00057/) (2022)

## Abstract (arXiv)

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.