Benchmarking VLMs' Reasoning About Persuasive Atypical Images

Abstract

Vision language models (VLMs) have shown strong zero-shot generalization across various tasks especially when integrated with large language models (LLMs). However their ability to comprehend rhetorical and persuasive visual media such as advertisements remains understudied. Ads often employ atypical imagery using surprising object juxtapositions to convey shared properties. For example Fig. 1 (e) shows a beer with a feather-like texture. This requires advanced reasoning to deduce that this atypical representation signifies the beer's lightness. We introduce three novel tasks Multi-label Atypicality Classification Atypicality Statement Retrieval and Atypical Object Recognition to benchmark VLMs' understanding of atypicality in persuasive images. We evaluate how well VLMs use atypicality to infer an ad's message and test their reasoning abilities by employing semantically challenging negatives. Finally we pioneer atypicality-aware verbalization by extracting comprehensive image descriptions sensitive to atypical elements. Findings reveal that: (1) VLMs lack advanced reasoning capabilities compared to LLMs; (2) simple effective strategies can extract atypicality-aware information leading to comprehensive image verbalization; (3) atypicality aids persuasive ad understanding. Code and data is available at aysanaghazadeh.github.io/PersuasiveAdVLMBenchmark/