Enhanced zero-shot image classification with LLM assistance

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.