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Conformer

Conformer: Local Features Coupling Global Representations for Visual Recognition

Abstract

Within Convolutional Neural Network (CNN), the convolution operations are good at extracting local features but experience difficulty to capture global representations. Within visual transformer, the cascaded self-attention modules can capture long-distance feature dependencies but unfortunately deteriorate local feature details. In this paper, we propose a hybrid network structure, termed Conformer, to take advantage of convolutional operations and self-attention mechanisms for enhanced representation learning. Conformer roots in the Feature Coupling Unit (FCU), which fuses local features and global representations under different resolutions in an interactive fashion. Conformer adopts a concurrent structure so that local features and global representations are retained to the maximum extent. Experiments show that Conformer, under the comparable parameter complexity, outperforms the visual transformer (DeiT-B) by 2.3% on ImageNet. On MSCOCO, it outperforms ResNet-101 by 3.7% and 3.6% mAPs for object detection and instance segmentation, respectively, demonstrating the great potential to be a general backbone network.

Results and models

ImageNet-1k

Model

Params(M)

Flops(G)

Top-1 (%)

Top-5 (%)

Config

Download

Conformer-tiny-p16*

23.52

4.90

81.31

95.60

config

model

Conformer-small-p32*

38.85

7.09

81.96

96.02

config

model

Conformer-small-p16*

37.67

10.31

83.32

96.46

config

model

Conformer-base-p16*

83.29

22.89

83.82

96.59

config

model

Models with * are converted from the official repo. The config files of these models are only for validation. We don’t ensure these config files’ training accuracy and welcome you to contribute your reproduction results.

Citation

@article{peng2021conformer,
      title={Conformer: Local Features Coupling Global Representations for Visual Recognition},
      author={Zhiliang Peng and Wei Huang and Shanzhi Gu and Lingxi Xie and Yaowei Wang and Jianbin Jiao and Qixiang Ye},
      journal={arXiv preprint arXiv:2105.03889},
      year={2021},
}
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