Research talk
Enhanced zero-shot image classification with LLM assistance
May 31, 2024
12:00 pm - 1:00 pm Pacific Time
Register now
During this research talk, you’ll see how follow-up differential descriptions (FuDD) can enhance zero-shot image classification by tailoring class descriptions for each dataset.
PhD Student Reza Esfandiarpoor from Brown University will discuss how FuDD identifies ambiguous classes for each image and then employs a large language model (LLM) to produce new descriptions that better distinguish them.
The talk will address:
- How FuDD performs compared to few-shot adaptation methods.
- What challenges FuDD performs well on.
- How to use FuDD in your workflow.
Speakers
Reza Esfandiarpoor
PhD Student
Brown University
Reza Esfandiarpoor is a fifth-year Ph.D. candidate in the Department of Computer Science at Brown University, advised by Stephen Bach. His research interests concern machine learning systems with multiple large pre-trained models and the new challenges and opportunities that interactions between these models provide.