MaiT: Leverage Attention Masks for More Efficient Image Transformers
This addresses efficiency issues for embedded applications in computer vision, but it is incremental as it builds on existing transformer architectures.
The paper tackles the efficiency problem in image transformers by introducing attention masks to incorporate spatial locality, resulting in up to 1.7% higher top-1 accuracy with fewer parameters and FLOPs and up to 1.5X improved throughput.
Though image transformers have shown competitive results with convolutional neural networks in computer vision tasks, lacking inductive biases such as locality still poses problems in terms of model efficiency especially for embedded applications. In this work, we address this issue by introducing attention masks to incorporate spatial locality into self-attention heads. Local dependencies are captured efficiently with masked attention heads along with global dependencies captured by unmasked attention heads. With Masked attention image Transformer - MaiT, top-1 accuracy increases by up to 1.7% compared to CaiT with fewer parameters and FLOPs, and the throughput improves by up to 1.5X compared to Swin. Encoding locality with attention masks is model agnostic, and thus it applies to monolithic, hierarchical, or other novel transformer architectures.