Learning Concise and Descriptive Attributes for Visual Recognition

Understand

Recent advances in foundation models present new opportunities for interpretable visual recognition -- one can first query Large Language Models (LLMs) to obtain a set of attributes that describe each class, then apply vision-language models to classify images via these attributes.

• Pioneering work shows that querying thousands of attributes can achieve performance competitive with image features.
• However, our further investigation on 8 datasets reveals that LLM-generated attributes in a large quantity perform almost the same as random words.
• This surprising finding suggests that significant noise may be present in these attributes.