  Transformer recently has presented 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, including (1) linear complexity
attention layer, (2) overlapping patch embedding, and (3) convolutional
feed-forward network. With these modifications, PVT v2 reduces the
computational complexity of PVT v1 to linear and achieves significant
improvements on fundamental vision tasks such as classification, detection, and
segmentation. Notably, the proposed PVT v2 achieves comparable or better
performances than recent works such as Swin Transformer. We hope this work will
facilitate state-of-the-art Transformer researches in computer vision. Code is
available at https://github.com/whai362/PVT.
