# MMMORRF: Multimodal Multilingual Modularized Reciprocal Rank Fusion

arXiv: https://arxiv.org/abs/2503.20698 · HTML: https://mlsanity.com/paper/2503.20698/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Video understanding with MLLMs, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 25 of its 40 references that are in the map.
- First posted on arXiv 26 Mar 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Video understanding with MLLMs](https://mlsanity.com/mllm/video-understanding-with-mllms/)

## Builds on (most cited first, 25 of 25)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval](https://mlsanity.com/paper/2104.00650/) (2021)
- [LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment](https://mlsanity.com/paper/2310.01852/) (2023)
- [CLIP2Video: Mastering Video-Text Retrieval via Image CLIP](https://mlsanity.com/paper/2106.11097/) (2021)
- [TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval](https://mlsanity.com/paper/2001.09099/) (2020)
- [Real-time Scene Text Detection with Differentiable Binarization](https://mlsanity.com/paper/1911.08947/) (2019)
- [VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset](https://mlsanity.com/paper/2305.18500/) (2023)
- [Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion](https://mlsanity.com/paper/2202.10304/) (2022)
- [Cap4Video: What Can Auxiliary Captions Do for Text-Video Retrieval?](https://mlsanity.com/paper/2301.00184/) (2022)
- [A Comprehensive Survey on Cross-modal Retrieval](https://mlsanity.com/paper/1607.06215/) (2016)
- [Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations](https://mlsanity.com/paper/2211.11427/) (2022)
- [Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models](https://mlsanity.com/paper/2201.08471/) (2022)
- [A Large Cross-Modal Video Retrieval Dataset with Reading Comprehension](https://mlsanity.com/paper/2305.03347/) (2023)
- [Translate-Distill: Learning Cross-Language Dense Retrieval by Translation and Distillation](https://mlsanity.com/paper/2401.04810/) (2024)
- [Neural Approaches to Multilingual Information Retrieval](https://mlsanity.com/paper/2209.01335/) (2022)
- [MultiVENT 2.0: A Massive Multilingual Benchmark for Event-Centric Video Retrieval](https://mlsanity.com/paper/2410.11619/) (2024)
- [Learning Cross-Lingual IR from an English Retriever](https://mlsanity.com/paper/2112.08185/) (2021)
- [RAP: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter](https://mlsanity.com/paper/2405.19465/) (2024)
- [C3: Continued Pretraining with Contrastive Weak Supervision for Cross Language Ad-Hoc Retrieval](https://mlsanity.com/paper/2204.11989/) (2022)
- [A Survey of Video Datasets for Grounded Event Understanding](https://mlsanity.com/paper/2406.09646/) (2024)
- [MultiVENT: Multilingual Videos of Events with Aligned Natural Text](https://mlsanity.com/paper/2307.03153/) (2023)
- [MUSE: Mamba is Efficient Multi-scale Learner for Text-video Retrieval](https://mlsanity.com/paper/2408.10575/) (2024)
- [Distillation for Multilingual Information Retrieval](https://mlsanity.com/paper/2405.00977/) (2024)

## Built on by (most cited first, 8 of 8)

- [WikiVideo: Article Generation from Multiple Videos](https://mlsanity.com/paper/2504.00939/) (2025)
- [Seeing Through the MiRAGE: Evaluating Multimodal Retrieval Augmented Generation](https://mlsanity.com/paper/2510.24870/) (2025)
- [RANKVIDEO: Reasoning Reranking for Text-to-Video Retrieval](https://mlsanity.com/paper/2602.02444/) (2026)
- [Beyond Relevance: On the Relationship Between Retrieval and RAG Information Coverage](https://mlsanity.com/paper/2603.08819/) (2026)
- [CRAFT: Critic-Refined Adaptive Key-Frame Targeting for Multimodal Video Question Answering](https://mlsanity.com/paper/2605.19075/) (2026)
- [Hawk: Harnessing Hardware-Aware Knowledge for High-Performance NPU Kernel Generation](https://mlsanity.com/paper/2607.01590/) (2026)
- [MARQUIS: A Three-Stage Pipeline for Video Retrieval-Augmented Generation](https://mlsanity.com/paper/2605.17640/) (2026)
- [MultiVENT-Raw: A Benchmark for Retrieval and Reasoning over Raw Videos](https://mlsanity.com/paper/2609.28437/) (2026)

## Abstract (arXiv)

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.