Abstract
This guided research focuses on prompt and description selection for CLIP-style zero-shot classification. The project analyzes how class descriptions affect recognition quality and investigates automatic selection strategies to improve robustness across classes.
Topic
Investigate CLIP-driven methods for selecting class descriptions/prompts that maximize zero-shot classification performance while maintaining stability across datasets and class granularity.
Tasks
- Build a baseline zero-shot CLIP evaluation pipeline.
- Implement and compare description selection strategies.
- Analyze performance sensitivity to prompt wording and class semantics.