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Self-Attention for Computer Vision
Aravind Srinivas · Prajit Ramachandran · Ashish Vaswani

Mon Jul 19 08:00 PM -- 10:45 PM (PDT) @ Virtual

The tutorial will be about the application of self-attention mechanisms in computer vision. Self-Attention has been widely adopted in NLP, with the fully attentional Transformer model having largely replaced RNNs and now being used in state-of-the-art language understanding models like GPT, BERT, XLNet, T5, Electra, and Meena. Thus, there has been a tremendous interest in studying whether self-attention can have a similarly big and far-reaching impact in computer vision. However, vision tasks have different properties compared to language tasks, so a lot of research has been devoted to exploring the best way to apply self-attention to visual models. This tutorial will cover many of the different applications of self-attention in vision in order to give the viewer a broad and precise understanding of this subfield.

Author Information

Aravind Srinivas (UC Berkeley)
Prajit Ramachandran (Google)
Ashish Vaswani (Google Brain)

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