Positional Label for Self-Supervised Vision Transformer

Positional encoding is important for vision transformer (ViT) to capture the spatial structure of the input image. General effectiveness has been proven in ViT. In our work we propose to train ViT to recognize the positional label of patches of the input image, this apparently simple task actually yields a meaningful self-supervisory task. Based on previous work on ViT positional encoding, we propose two positional labels dedicated to 2D images including absolute position and relative position. Our positional labels can be easily plugged into various current ViT variants. It can work in two ways: (a) As an auxiliary training target for vanilla ViT for better performance. (b) Combine the self-supervised ViT to provide a more powerful self-supervised signal for semantic feature learning. Experiments demonstrate that with the proposed self-supervised methods, ViT-B and Swin-B gain improvements of 1.20% (top-1 Acc) and 0.74% (top-1 Acc) on ImageNet, respectively, and 6.15% and 1.14% improvement on Mini-ImageNet. The code is publicly available at: https://github.com/zhangzhemin/PositionalLabel.

Authors

• Zhemin Zhang School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan, China
• Xun Gong School of Computing and Artificial Intelligence, Southwest Jiaotong University Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province, Southwest Jiaotong University

Keywords

• CV: Object Detection & Categorization
• CV: Representation Learning for Vision