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Though image transformers have shown competitive results with convolutionalneural networks in computer vision tasks, lacking inductive biases such aslocality still poses problems in terms of model efficiency especially forembedded applications. In this work, we address this issue by introducingattention masks to incorporate spatial locality into self-attention heads.Local dependencies are captured efficiently with masked attention heads alongwith global dependencies captured by unmasked attention heads. With Maskedattention image Transformer - MaiT, top-1 accuracy increases by up to 1.7%compared to CaiT with fewer parameters and FLOPs, and the throughput improvesby up to 1.5X compared to Swin. Encoding locality with attention masks is modelagnostic, and thus it applies to monolithic, hierarchical, or other noveltransformer architectures.

MaiT: Leverage Attention Masks for More Efficient Image Transformers