Long-Tailed Visual Recognition via Improved Cross-Window Self-Attention and TrivialAugment

In the real world, large-scale image data sets usually present long-tailed distribution. When traditional visual recognition methods are applied to long-tail image data sets, problems such as model failure and sudden decline in recognition accuracy occur. While, when deep learning models encounter long-tailed datasets, they tend to perform poorly. In order to mitigate the impact of these problems, we propose CWTA (Long-tailed Visual Recognition via improved Cross-Window Self-Attention and TrivialAugment). CWTA uses CNN to better capture the local features of the image, uses the Cross-Window Self-Attention mechanism to dynamically adjust the perception domain to better deal with image noise, and uses TrivialAugment to enhance the diversity of a few types of data samples, thus improving the recognition accuracy of long-tailed distributed images. The experimental results show that the proposed CWTA performs best in the classification accuracy of different categories on different long-tailed datasets. We also compared CWTA with other long-tailed recognition algorithms (such as OLTR, LWS, ResLT, PaCo, and BALLAD), and the CWTA is the best when ResNet-50 as the Backbone. On the CIFAR100-LT, ImageNet-LT, and Places-LT datasets, the acc of all categories of CWTA is 12.9%, 0.4%, and 1.3% higher than that of BALLAD, respectively. For F1-Score on CIFAR100-LT, ImageNet-LT, and Places-LT datasets, CWTA is 6.6%, 2.2%, and 1.5% higher than BALLAD, respectively.