PVT v2: Improved baselines with Pyramid Vision Transformer - Wang, Wenhai; Xie, Enze; Li, Xiang; Fan, Deng-Ping; Song, Kaitao; Liang, Ding; Lu, Tong; Luo, Ping; Shao, Ling (Computational visual media (Beijing), 2022)
Transformers have recently lead to encouraging progress in computer vision. In this work, we present new baselines by improving the original Pyramid Vision Transformer (PVT v1) by adding three designs: (i) a linear complexity attention layer, (ii) an overlapping patch embedding, and (iii) a convolutional feed-forward network. With these modifications, PVT v2 reduces the computational complexity of PVT v1 to linearity and provides significant improvements on fundamental vision tasks such as classification, detection, and segmentation. In particular, PVT v2 achieves comparable or better performance than recent work such as the Swin transformer. We hope this work will facilitate state-of-the-art transformer research in computer vision. Code is available at https://github.com/whai362/PVT .