Exploring and Improving Mobile Level Vision Transformers
This work addresses the challenge of deploying vision transformers efficiently on mobile devices, representing an incremental improvement in domain-specific optimization.
The paper tackled the performance drop of vision transformers in mobile-level settings by proposing an irregular patch embedding module and an adaptive patch fusion module, which improved the DeiT baseline by over 9% and surpassed other architectures like Swin and CoaT.
We study the vision transformer structure in the mobile level in this paper, and find a dramatic performance drop. We analyze the reason behind this phenomenon, and propose a novel irregular patch embedding module and adaptive patch fusion module to improve the performance. We conjecture that the vision transformer blocks (which consist of multi-head attention and feed-forward network) are more suitable to handle high-level information than low-level features. The irregular patch embedding module extracts patches that contain rich high-level information with different receptive fields. The transformer blocks can obtain the most useful information from these irregular patches. Then the processed patches pass the adaptive patch merging module to get the final features for the classifier. With our proposed improvements, the traditional uniform vision transformer structure can achieve state-of-the-art results in mobile level. We improve the DeiT baseline by more than 9\% under the mobile-level settings and surpass other transformer architectures like Swin and CoaT by a large margin.