Official implement of "CAT: Cross Attention in Vision Transformer".

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Deep LearningCAT
Overview

CAT: Cross Attention in Vision Transformer

This is official implement of "CAT: Cross Attention in Vision Transformer".

Abstract

Since Transformer has found widespread use in NLP, the potential of Transformer in CV has been realized and has inspired many new approaches. However, the computation required for replacing word tokens with image patches for Transformer after the tokenization of the image is vast(e.g., ViT), which bottlenecks model training and inference. In this paper, we propose a new attention mechanism in Transformer termed Cross Attention, which alternates attention inner the image patch instead of the whole image to capture local information and apply attention between image patches which are divided from single-channel feature maps to capture global information. Both operations have less computation than standard self-attention in Transformer. By alternately applying attention inner patch and between patches, we implement cross attention to maintain the performance with lower computational cost and build a hierarchical network called Cross Attention Transformer(CAT) for other vision tasks. Our base model achieves state-of-the-arts on ImageNet-1K, and improves the performance of other methods on COCO and ADE20K, illustrating that our network has the potential to serve as general backbones.

CAT achieves strong performance on COCO object detection(implemented with mmdectection) and ADE20K semantic segmentation(implemented with mmsegmantation).

architecture

Pretrained Models and Results on ImageNet-1K

name resolution [email protected] [email protected] #params FLOPs model log
CAT-T 224x224 80.3 95.0 17M 2.8G github github
CAT-S* 224x224 81.8 95.6 37M 5.9G github github
CAT-B 224x224 82.8 96.1 52M 8.9G github github
CAT-T-v2 224x224 81.7 95.5 36M 3.9G Coming Coming

Note: * indicates new version of model and log.

Models and Results on Object Detection (COCO 2017 val)

Backbone Method pretrain Lr Schd box mAP mask mAP #params FLOPs model log
CAT-S Mask R-CNN+ ImageNet-1K 1x 41.6 38.6 57M 295G github github
CAT-B Mask R-CNN+ ImageNet-1K 1x 41.8 38.7 71M 356G github github
CAT-S FCOS ImageNet-1K 1x 40.0 - 45M 245G github github
CAT-B FCOS ImageNet-1K 1x 41.0 - 59M 303G github github
CAT-S ATSS ImageNet-1K 1x 42.0 - 45M 243G github github
CAT-B ATSS ImageNet-1K 1x 42.5 - 59M 303G github github
CAT-S RetinaNet ImageNet-1K 1x 40.1 - 47M 276G github github
CAT-B RetinaNet ImageNet-1K 1x 41.4 - 62M 337G github github
CAT-S Cascade R-CNN ImageNet-1K 1x 44.1 - 82M 270G github github
CAT-B Cascade R-CNN ImageNet-1K 1x 44.8 - 96M 330G github github
CAT-S Cascade R-CNN+ ImageNet-1K 1x 45.2 - 82M 270G github github
CAT-B Cascade R-CNN+ ImageNet-1K 1x 46.3 - 96M 330G github github

Note: + indicates multi-scale training.

Models and Results on Semantic Segmentation (ADE20K val)

Backbone Method pretrain Crop Size Lr Schd mIoU mIoU (ms+flip) #params FLOPs model log
CAT-S Semantic FPN ImageNet-1K 512x512 80K 40.6 42.1 41M 214G github github
CAT-B Semantic FPN ImageNet-1K 512x512 80K 42.2 43.6 55M 276G github github
CAT-S Semantic FPN ImageNet-1K 512x512 160K 42.2 42.8 41M 214G github github
CAT-B Semantic FPN ImageNet-1K 512x512 160K 43.2 44.9 55M 276G github github

Citing CAT

You can cite the paper as:

@article{lin2021cat,
  title={CAT: Cross Attention in Vision Transformer},
  author={Hezheng Lin and Xing Cheng and Xiangyu Wu and Fan Yang and Dong Shen and Zhongyuan Wang and Qing Song and Wei Yuan},
  journal={arXiv preprint arXiv:2106.05786},
  year={2021}
}

Started

Please refer to get_started.

Acknowledgement

Our implementation is mainly based on Swin.

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