Vision Transformer and its Applications

Vision transformer is a recent breakthrough in the area of computer vision. While transformer-based models have dominated the field of natural language processing since 2017, CNN-based models are still demonstrating state-of-the-art performances in vision problems. Last year, a group of researchers from Google figured out how to make a transformer work on recognition. They called it "vision transformer". The follow-up works by the community demonstrated superior performance of vision transformers not only in recognition but also in other downstream tasks such as detection, segmentation, multi-modal learning and scene text recognition to mention a few.
In this talk, Rowel Atienza will go into a deeper understanding of the model architecture of vision transformers. Most importantly, Rowel will focus on the concept of self-attention and its role in vision. Then, he will present different model implementations utilizing the vision transformer as the main backbone.
Since self-attention can be applied beyond transformers, Rowel Atienza will also discuss a promising direction in building general-purpose model architectures. In particular, networks that can process a variety of data formats such as text, audio, image and video.

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