Transformer has achieved great success in computer vision, while
how to split patches in an image remains a problem. Existing methods usually use a fixed-size patch embedding which might destroy
the semantics of objects. To address this problem, we propose a new
Deformable Patch (DePatch) module which learns to adaptively
split the images into patches with different positions and scales in
a data-driven way rather than using predefined fixed patches. In
this way, our method can well preserve the semantics in patches.
The DePatch module can work as a plug-and-play module, which
can easily be incorporated into different transformers to achieve
an end-to-end training. We term this DePatch-embedded transformer as Deformable Patch-based Transformer (DPT) and conduct
extensive evaluations of DPT on image classification and object
detection. Results show DPT can achieve 81.9% top-1 accuracy on
ImageNet classification, and 43.7% box mAP with RetinaNet, 44.3%
with Mask R-CNN on MSCOCO object detection. Code has been
made available at: https://github.com/CASIA-IVA-Lab/DPT.