Transformer in Transformer

Transformer is a new kind of neural architecture which encodes the input data as powerful features via the attention mechanism. Basically, the visual transformers first divide the input images into several local patches and then calculate both representations and their relationship. Since natural images are of high complexity with abundant detail and color information, the granularity of the patch dividing is not fine enough for excavating features of objects in different scales and locations. In this paper, we point out that the attention inside these local patches are also essential for building visual transformers with high performance and we explore a new architecture, namely, Transformer iN Transformer (TNT). Specifically, we regard the local patches (e.g., 16$\times$16) as "visual sentences" and present to further divide them into smaller patches (e.g., 4$\times$4) as "visual words". The attention of each word will be calculated with other words in the given visual sentence with negligible computational costs. Features of both words and sentences will be aggregated to enhance the representation ability. Experiments on several benchmarks 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 similar computational cost. The PyTorch code is available at https://github.com/huawei-noah/CV-Backbones, and the MindSpore code is available at https://gitee.com/mindspore/models/tree/master/research/cv/TNT.

PDF Abstract NeurIPS 2021 PDF NeurIPS 2021 Abstract
Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Image Classification CIFAR-10 TNT-B Percentage correct 99.1 # 6
PARAMS 65.6M # 205
Image Classification CIFAR-100 TNT-B Percentage correct 91.1 # 19
PARAMS 65.6M # 166
Image Classification ImageNet TNT-B Top 1 Accuracy 83.9% # 189
Number of params 65.6M # 487
Hardware Burden None # 1
Operations per network pass None # 1
Fine-Grained Image Classification Oxford 102 Flowers TNT-B Accuracy 99.0% # 6
PARAMS 65.6M # 21
Fine-Grained Image Classification Oxford-IIIT Pets TNT-B Accuracy 95.0% # 6
PARAMS 65.6M # 23

Methods