Timezone: »

Inception Transformer
Chenyang Si · Weihao Yu · Pan Zhou · Yichen Zhou · Xinchao Wang · Shuicheng Yan

Recent studies show that transformer has strong capability of building long-range dependencies, yet is incompetent in capturing high frequencies that predominantly convey local information. To tackle this issue, we present a novel and general-purpose $\textit{Inception Transformer}$, or $\textit{iFormer}$ for short, that effectively learns comprehensive features with both high- and low-frequency information in visual data. Specifically, we design an Inception mixer to explicitly graft the advantages of convolution and max-pooling for capturing the high-frequency information to transformers. Different from recent hybrid frameworks, the Inception mixer brings greater efficiency through a channel splitting mechanism to adopt parallel convolution/max-pooling path and self-attention path as high- and low-frequency mixers, while having the flexibility to model discriminative information scattered within a wide frequency range. Considering that bottom layers play more roles in capturing high-frequency details while top layers more in modeling low-frequency global information, we further introduce a frequency ramp structure, i.e., gradually decreasing the dimensions fed to the high-frequency mixer and increasing those to the low-frequency mixer, which can effectively trade-off high- and low-frequency components across different layers. We benchmark the iFormer on a series of vision tasks, and showcase that it achieves impressive performance on image classification, COCO detection and ADE20K segmentation. For example, our iFormer-S hits the top-1 accuracy of 83.4% on ImageNet-1K, much higher than DeiT-S by 3.6%, and even slightly better than much bigger model Swin-B (83.3%) with only 1/4 parameters and 1/3 FLOPs. Code and models will be released.

Author Information

Chenyang Si (Sea AI Lab)
Weihao Yu (National University of Singapore)
Pan Zhou (SEA AI Lab)

Currently, I am a senior Research Scientist in Sea AI Lab of Sea group. Before, I worked in Salesforce as a research scientist during 2019 to 2021. I completed my Ph.D. degree in 2019 at the National University of Singapore (NUS), fortunately advised by Prof. Jiashi Feng and Prof. Shuicheng Yan. Before studying in NUS, I graduated from Peking University (PKU) in 2016 and during this period, I was fortunately directed by Prof. Zhouchen Lin and Prof. Chao Zhang in ZERO Lab. During the research period, I also work closely with Prof. Xiaotong Yuan. I also spend several wonderful months in 2018 at Georgia Tech as visiting student hosted by Prof. Huan Xu.

Yichen Zhou (Sea Group)
Xinchao Wang
Shuicheng Yan (Sea AI Lab)

Related Events (a corresponding poster, oral, or spotlight)

More from the Same Authors