RAVENEA: A Benchmark for Multimodal Retrieval-Augmented Visual Culture Understanding

• URL: http://arxiv.org/abs/2505.14462v1
• Date: Tue, 20 May 2025 14:57:16 GMT
• Title: RAVENEA: A Benchmark for Multimodal Retrieval-Augmented Visual Culture Understanding
• Authors: Jiaang Li, Yifei Yuan, Wenyan Li, Mohammad Aliannejadi, Daniel Hershcovich, Anders Søgaard, Ivan Vulić, Wenxuan Zhang, Paul Pu Liang, Yang Deng, Serge Belongie,
• Abstract summary: We introduce RAVENEA, a new benchmark designed to advance visual culture understanding through retrieval.<n>RAVENEA focuses on two tasks: culture-focused visual question answering (cVQA) and culture-informed image captioning (cIC)<n>We train and evaluate seven multimodal retrievers for each image query, and measure the downstream impact of retrieval-augmented inputs across fourteen state-of-the-art vision-language models.
• Score: 79.44246283490665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As vision-language models (VLMs) become increasingly integrated into daily life, the need for accurate visual culture understanding is becoming critical. Yet, these models frequently fall short in interpreting cultural nuances effectively. Prior work has demonstrated the effectiveness of retrieval-augmented generation (RAG) in enhancing cultural understanding in text-only settings, while its application in multimodal scenarios remains underexplored. To bridge this gap, we introduce RAVENEA (Retrieval-Augmented Visual culturE uNdErstAnding), a new benchmark designed to advance visual culture understanding through retrieval, focusing on two tasks: culture-focused visual question answering (cVQA) and culture-informed image captioning (cIC). RAVENEA extends existing datasets by integrating over 10,000 Wikipedia documents curated and ranked by human annotators. With RAVENEA, we train and evaluate seven multimodal retrievers for each image query, and measure the downstream impact of retrieval-augmented inputs across fourteen state-of-the-art VLMs. Our results show that lightweight VLMs, when augmented with culture-aware retrieval, outperform their non-augmented counterparts (by at least 3.2% absolute on cVQA and 6.2% absolute on cIC). This highlights the value of retrieval-augmented methods and culturally inclusive benchmarks for multimodal understanding.

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