Benchmarking Zero-Shot Recognition with Vision-Language Models: Challenges on Granularity and Specificity

TL;DR

This paper introduces new benchmarks to evaluate vision-language models' ability to handle concept granularity and text specificity in zero-shot recognition, revealing their limitations in real-world scenarios.

Contribution

It proposes novel benchmarks for assessing VLMs on granularity and specificity, highlighting their current shortcomings and guiding future improvements.

Findings

VLMs prefer moderately fine-grained concepts

VLMs struggle with text specificity and subtle differences

Fine-tuning partially improves performance but doesn't fully solve issues

Abstract

This paper presents novel benchmarks for evaluating vision-language models (VLMs) in zero-shot recognition, focusing on granularity and specificity. Although VLMs excel in tasks like image captioning, they face challenges in open-world settings. Our benchmarks test VLMs' consistency in understanding concepts across semantic granularity levels and their response to varying text specificity. Findings show that VLMs favor moderately fine-grained concepts and struggle with specificity, often misjudging texts that differ from their training data. Extensive evaluations reveal limitations in current VLMs, particularly in distinguishing between correct and subtly incorrect descriptions. While fine-tuning offers some improvements, it doesn't fully address these issues, highlighting the need for VLMs with enhanced generalization capabilities for real-world applications. This study provides insights into VLM limitations and suggests directions for developing more robust models.