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Stand-Alone Self-Attention in Vision Models


niki parmar: New Paper: Stand-Alone Self-Attention in Vision Models Can attention work as a stand-alone primitive for vision models? We develop a pure self-attention model by replacing the spatial convolutions in a ResNet by a simple, local self-attention layer.

15 replies, 460 likes

niki parmar: Our paper got accepted to #Neurips!! Code release coming soon, keep an eye out :)

2 replies, 206 likes

William Fedus: Additional evidence of the transformer/self-attention as a useful computational primitive in vision tasks such as ImageNet classification and COCO detection. Future work is exciting: "...we hope to unify convolution and self-attention to best combine their unique advantages"

0 replies, 23 likes

Jean-Baptiste Cordonnier: Our work explains the recent success of Transformer architecture applied to vision: Attention Augmented Convolutional Networks. @IrwanBello et al., 2019. Stand-Alone Self-Attention in Vision Models. Ramachandran et al., 2019. 3/5

1 replies, 17 likes

Ashish Vaswani: Pure content based interactions are competitive for vision models. Lot's of exciting work to be done in this research area.

2 replies, 14 likes

Daisuke Okanohara: For image recognition tasks, they showed that local self-attention is competitive or superior to convolution in higher layers, and full attention model can achieve similar performance as ConvNet. Better absolute/relative position encoding is required.

0 replies, 8 likes

HotComputerScience: Most popular computer science paper of the day: "Stand-Alone Self-Attention in Vision Models"

0 replies, 4 likes


Found on Jun 17 2019 at

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