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Transformer Models for Vision

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Transformer Models for Vision

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Crowding reveals fundamental differences in local vs. global processing in humans and machines

Unsupervised Stereo Matching Using Confidential Correspondence Consistency

Deep learning-based reduced order models in cardiac electrophysiology

Assessing Public Opinion on CRISPR-Cas9: Combining Crowdsourcing and Deep Learning

STC-GAN: Spatio-Temporally Coupled Generative Adversarial Networks for Predictive Scene Parsing

Neural Network Based End-to-End Query by Example Spoken Term Detection

Joint Human Pose Estimation and Stereo 3D Localization

Benefiting from Multitask Learning to Improve Single Image Super-Resolution

Self-supervised learning for time series classification

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention