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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