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Poster
Peripheral Vision Transformer
Juhong Min · Yucheng Zhao · Chong Luo · Minsu Cho

Wed Nov 30 09:00 AM -- 11:00 AM (PST) @ Hall J #637
in Poster Session 3 »

Human vision possesses a special type of visual processing systems called peripheral vision. Partitioning the entire visual field into multiple contour regions based on the distance to the center of our gaze, the peripheral vision provides us the ability to perceive various visual features at different regions. In this work, we take a biologically inspired approach and explore to model peripheral vision in deep neural networks for visual recognition. We propose to incorporate peripheral position encoding to the multi-head self-attention layers to let the network learn to partition the visual field into diverse peripheral regions given training data. We evaluate the proposed network, dubbed PerViT, on ImageNet-1K and systematically investigate the inner workings of the model for machine perception, showing that the network learns to perceive visual data similarly to the way that human vision does. The performance improvements in image classification over the baselines across different model sizes demonstrate the efficacy of the proposed method.

Keywords: vision transformers · image classification · Image recognition · Peripheral vision · inductive bias
[ Paper]  [ Poster]  [ OpenReview] 

Author Information

Juhong Min (POSTECH)
Yucheng Zhao (University of Science and Technology of China)
Chong Luo (MSRA)
Minsu Cho (POSTECH)

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