Waffling around for Performance: Visual Classification with Random Words and Broad Concepts

• URL: http://arxiv.org/abs/2306.07282v2
• Date: Thu, 17 Aug 2023 02:27:32 GMT
• Title: Waffling around for Performance: Visual Classification with Random Words and Broad Concepts
• Authors: Karsten Roth, Jae Myung Kim, A. Sophia Koepke, Oriol Vinyals, Cordelia Schmid, Zeynep Akata
• Abstract summary: WaffleCLIP is a framework for zero-shot visual classification which simply replaces LLM-generated descriptors with random character and word descriptors. We conduct an extensive experimental study on the impact and shortcomings of additional semantics introduced with LLM-generated descriptors.
• Score: 121.60918966567657
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The visual classification performance of vision-language models such as CLIP has been shown to benefit from additional semantic knowledge from large language models (LLMs) such as GPT-3. In particular, averaging over LLM-generated class descriptors, e.g. "waffle, which has a round shape", can notably improve generalization performance. In this work, we critically study this behavior and propose WaffleCLIP, a framework for zero-shot visual classification which simply replaces LLM-generated descriptors with random character and word descriptors. Without querying external models, we achieve comparable performance gains on a large number of visual classification tasks. This allows WaffleCLIP to both serve as a low-cost alternative, as well as a sanity check for any future LLM-based vision-language model extensions. We conduct an extensive experimental study on the impact and shortcomings of additional semantics introduced with LLM-generated descriptors, and showcase how - if available - semantic context is better leveraged by querying LLMs for high-level concepts, which we show can be done to jointly resolve potential class name ambiguities. Code is available here: https://github.com/ExplainableML/WaffleCLIP.

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