Best View
"Best View" research encompasses diverse efforts to optimize data representation and processing across various modalities, aiming to improve accuracy, efficiency, and robustness in tasks ranging from object recognition and scene understanding to medical imaging and brain-computer interfaces. Current research focuses on developing novel architectures, such as generative adversarial networks and transformers, and employing techniques like contrastive learning and curriculum learning to generate informative and diverse data representations. These advancements have significant implications for improving the performance of machine learning models and enabling new applications in diverse fields, including healthcare, robotics, and augmented reality.
Papers
From My View to Yours: Ego-Augmented Learning in Large Vision Language Models for Understanding Exocentric Daily Living Activities
Dominick Reilly, Manish Kumar Govind, Srijan Das
The Impact of Model Scaling on Seen and Unseen Language Performance
Rhitabrat Pokharel, Sina Bagheri Nezhad, Ameeta Agrawal, Suresh Singh
Generative Adversarial Networks in Ultrasound Imaging: Extending Field of View Beyond Conventional Limits
Matej Gazda, Samuel Kadoury, Jakub Gazda, Peter Drotar
The Point of View of a Sentiment: Towards Clinician Bias Detection in Psychiatric Notes
Alissa A. Valentine, Lauren A. Lepow, Alexander W. Charney, Isotta Landi