123 citations · 503 across the 26 of their papers we have counts for
27 papers
The Limits of Fair Medical Imaging AI In The Wild
Yuzhe Yang, Haoran Zhang, Judy W Gichoya +2
As artificial intelligence (AI) rapidly approaches human-level performance in medical imaging, it is crucial that it does not exacerbate or propagate healthcare disparities. Prior…
Improving CLIP Training with Language Rewrites
Lijie Fan, Dilip Krishnan, Phillip Isola +2
Contrastive Language-Image Pre-training (CLIP) stands as one of the most effective and scalable methods for training transferable vision models using paired image and text data. CL…
Reparo: Loss-Resilient Generative Codec for Video Conferencing
Tianhong Li, Vibhaalakshmi Sivaraman, Pantea Karimi +3
Packet loss during video conferencing often results in poor quality and video freezing. Retransmitting lost packets is often impractical due to the need for real-time playback, and…
Change is Hard: A Closer Look at Subpopulation Shift
Yuzhe Yang, Haoran Zhang, Dina Katabi +1
Machine learning models often perform poorly on subgroups that are underrepresented in the training data. Yet, little is understood on the variation in mechanisms that cause subpop…
Contactless Oxygen Monitoring with Gated Transformer
Hao He, Yuan Yuan, Ying-Cong Chen +2
With the increasing popularity of telehealth, it becomes critical to ensure that basic physiological signals can be monitored accurately at home, with minimal patient overhead. In…
MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesis
Tianhong Li, Huiwen Chang, Shlok Kumar Mishra +3
Generative modeling and representation learning are two key tasks in computer vision. However, these models are typically trained independently, which ignores the potential for eac…