Scale-Aware Modulation Meet Transformer
This paper presents a new vision Transformer, Scale-Aware Modulation
Transformer (SMT), that can handle various downstream tasks efficiently by
combining the convolutional network and vision Transformer. The proposed
Scale-Aware Modulation (SAM) in the SMT includes two primary novel designs.
Firstly, we introduce the Multi-Head Mixed Convolution (MHMC) module, which can
capture multi-scale features and expand the receptive field. Secondly, we
propose the Scale-Aware Aggregation (SAA) module, which is lightweight but
effective, enabling information fusion across different heads. By leveraging
these two modules, convolutional modulation is further enhanced. Furthermore,
in contrast to prior works that utilized modulations throughout all stages to
build an attention-free network, we propose an Evolutionary Hybrid Network
(EHN), which can effectively simulate the shift from capturing local to global
dependencies as the network becomes deeper, resulting in superior performance.
Extensive experiments demonstrate that SMT significantly outperforms existing
state-of-the-art models across a wide range of visual tasks. Specifically, SMT
with 11.5M / 2.4GFLOPs and 32M / 7.7GFLOPs can achieve 82.2% and 84.3% top-1
accuracy on ImageNet-1K, respectively. After pretrained on ImageNet-22K in
224^2 resolution, it attains 87.1% and 88.1% top-1 accuracy when finetuned with
resolution 224^2 and 384^2, respectively. For object detection with Mask R-CNN,
the SMT base trained with 1x and 3x schedule outperforms the Swin Transformer
counterpart by 4.2 and 1.3 mAP on COCO, respectively. For semantic segmentation
with UPerNet, the SMT base test at single- and multi-scale surpasses Swin by
2.0 and 1.1 mIoU respectively on the ADE20K.
64b60dae786aaee1929d8a47
Weifeng Lin
64b60dae786aaee1929d8a48
Ziheng Wu
admin_assigned
2023-07-18T08:23:13.847Z
63b7bf7fca2f378e7103e182
/avatars/73b5179b888293a7039e9bc42d129c61.svg
wuziheng
user
wuziheng
64b60dae786aaee1929d8a49
Jiayu Chen
64b60dae786aaee1929d8a4a
Jun Huang
64b60dae786aaee1929d8a4b
Lianwen Jin
Scale-Aware Modulation Meet Transformer
This paper presents a new vision Transformer, Scale-Aware Modulation
Transformer (SMT), that can handle various downstream tasks efficiently by
combining the convolutional network and vision Transformer. The proposed
Scale-Aware Modulation (SAM) in the SMT includes two primary novel designs.
Firstly, we introduce the Multi-Head Mixed Convolution (MHMC) module, which can
capture multi-scale features and expand the receptive field. Secondly, we
propose the Scale-Aware Aggregation (SAA) module, which is lightweight but
effective, enabling information fusion across different heads. By leveraging
these two modules, convolutional modulation is further enhanced. Furthermore,
in contrast to prior works that utilized modulations throughout all stages to
build an attention-free network, we propose an Evolutionary Hybrid Network
(EHN), which can effectively simulate the shift from capturing local to global
dependencies as the network becomes deeper, resulting in superior performance.
Extensive experiments demonstrate that SMT significantly outperforms existing
state-of-the-art models across a wide range of visual tasks. Specifically, SMT
with 11.5M / 2.4GFLOPs and 32M / 7.7GFLOPs can achieve 82.2% and 84.3% top-1
accuracy on ImageNet-1K, respectively. After pretrained on ImageNet-22K in
224^2 resolution, it attains 87.1% and 88.1% top-1 accuracy when finetuned with
resolution 224^2 and 384^2, respectively. For object detection with Mask R-CNN,
the SMT base trained with 1x and 3x schedule outperforms the Swin Transformer
counterpart by 4.2 and 1.3 mAP on COCO, respectively. For semantic segmentation
with UPerNet, the SMT base test at single- and multi-scale surpasses Swin by
2.0 and 1.1 mIoU respectively on the ADE20K.
A Scale-Aware Modulation Transformer combines convolutional networks with attention mechanisms using Multi-Head Mixed Convolution and Scale-Aware Aggregation modules, achieving superior performance across visual tasks compared to existing models.
vision Transformer Scale-Aware Modulation Multi-Head Mixed Convolution Scale-Aware Aggregation Evolutionary Hybrid Network attention-free network ImageNet top-1 accuracy Mask R-CNN mAP UPerNet mIoU ADE20K