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Vision Transformers (ViT) serve as powerful vision models. Unlikeconvolutional neural networks, which dominated vision research in previousyears, vision transformers enjoy the ability to capture long-range dependenciesin the data. Nonetheless, an integral part of any transformer architecture, theself-attention mechanism, suffers from high latency and inefficient memoryutilization, making it less suitable for high-resolution input images. Toalleviate these shortcomings, hierarchical vision models locally employself-attention on non-interleaving windows. This relaxation reduces thecomplexity to be linear in the input size; however, it limits the cross-windowinteraction, hurting the model performance. In this paper, we propose a newshift-invariant local attention layer, called query and attend (QnA), thataggregates the input locally in an overlapping manner, much like convolutions.The key idea behind QnA is to introduce learned queries, which allow fast andefficient implementation. We verify the effectiveness of our layer byincorporating it into a hierarchical vision transformer model. We showimprovements in speed and memory complexity while achieving comparable accuracywith state-of-the-art models. Finally, our layer scales especially well withwindow size, requiring up-to x10 less memory while being up-to x5 faster thanexisting methods. The code is publicly available at\url{https://github.com/moabarar/qna}.

Learned Queries for Efficient Local Attention