2.2k citations · 7.1k across the 72 of their papers we have counts for
4 papers · 2 filters
Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers
John Guibas, Morteza Mardani, Zongyi Li +3
Vision transformers have delivered tremendous success in representation learning. This is primarily due to effective token mixing through self attention. However, this scales quadr…
Long-Short Transformer: Efficient Transformers for Language and Vision
Chen Zhu, Wei Ping, Chaowei Xiao +4
Transformers have achieved success in both language and vision domains. However, it is prohibitively expensive to scale them to long sequences such as long documents or high-resolu…
View Generalization for Single Image Textured 3D Models
Anand Bhattad, Aysegul Dundar, Guilin Liu +2
Humans can easily infer the underlying 3D geometry and texture of an object only from a single 2D image. Current computer vision methods can do this, too, but suffer from view gene…
Dual Contrastive Loss and Attention for GANs
Ning Yu, Guilin Liu, Aysegul Dundar +4
Generative Adversarial Networks (GANs) produce impressive results on unconditional image generation when powered with large-scale image datasets. Yet generated images are still eas…