MMMORRF: Multimodal Multilingual Modularized Reciprocal Rank Fusion

Videos inherently contain multiple modalities, including visual events, text overlays, sounds, and speech, all of which are important for retrieval. However, state-of-the-art multimodal language models like VAST and LanguageBind are built on vision-language models (VLMs), and thus overly prioritize visual signals. Retrieval benchmarks further reinforce this bias by focusing on visual queries and neglecting other modalities. We create a search system MMMORRF that extracts text and features from both visual and audio modalities and integrates them with a novel modality-aware weighted reciprocal rank fusion. MMMORRF is both effective and efficient, demonstrating practicality in searching videos based on users' information needs instead of visual descriptive queries. We evaluate MMMORRF on MultiVENT 2.0 and TVR, two multimodal benchmarks designed for more targeted information needs, and find that it improves nDCG@20 by 81% over leading multimodal encoders and 37% over single-modality retrieval, demonstrating the value of integrating diverse modalities.

@article{samuel2025_2503.20698,
  title={ MMMORRF: Multimodal Multilingual Modularized Reciprocal Rank Fusion },
  author={ Saron Samuel and Dan DeGenaro and Jimena Guallar-Blasco and Kate Sanders and Oluwaseun Eisape and Tanner Spendlove and Arun Reddy and Alexander Martin and Andrew Yates and Eugene Yang and Cameron Carpenter and David Etter and Efsun Kayi and Matthew Wiesner and Kenton Murray and Reno Kriz },
  journal={arXiv preprint arXiv:2503.20698},
  year={ 2025 }
}