Webinar series
Research talk
Enhanced zero-shot image classification with LLM assistance
May 31, 2024 | 12:00 PM - 1:00 PM Pacific Time
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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.
Presented by
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.
We look forward to seeing you!
Register now