Everything Can Be Described in Words: A Simple Unified Multi-Modal Framework with Semantic and Temporal Alignment

2025-03-12

AI Summary

To address challenges in long-video question answering (LVQA)—including extended temporal duration, sparse critical information, and difficult cross-modal retrieval—this paper proposes a “multimodal-as-text” paradigm: visual and audio modalities are unified into structured textual representations with simultaneous semantic and temporal alignment. Methodologically, we design an adaptive temporal segmentation and redundancy filtering mechanism, establishing the first retrievable and interpretable RAG framework supporting ultra-long videos (>1 hour). The framework integrates vision-language models (VLMs), automatic speech recognition (ASR), temporally aligned text embeddings, and vector database retrieval. On LVQA benchmarks, our approach significantly outperforms state-of-the-art methods, achieving substantial gains in sparse-information recall and cross-modal consistency, while maintaining high scalability and strong interpretability.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Question Answering

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Long Video Question Answering (LVQA) is challenging due to the need for temporal reasoning and large-scale multimodal data processing. Existing methods struggle with retrieving cross-modal information from long videos, especially when relevant details are sparsely distributed. We introduce UMaT (Unified Multi-modal as Text), a retrieval-augmented generation (RAG) framework that efficiently processes extremely long videos while maintaining cross-modal coherence. UMaT converts visual and auditory data into a unified textual representation, ensuring semantic and temporal alignment. Short video clips are analyzed using a vision-language model, while automatic speech recognition (ASR) transcribes dialogue. These text-based representations are structured into temporally aligned segments, with adaptive filtering to remove redundancy and retain salient details. The processed data is embedded into a vector database, enabling precise retrieval of dispersed yet relevant content. Experiments on a benchmark LVQA dataset show that UMaT outperforms existing methods in multimodal integration, long-form video understanding, and sparse information retrieval. Its scalability and interpretability allow it to process videos over an hour long while maintaining semantic and temporal coherence. These findings underscore the importance of structured retrieval and multimodal synchronization for advancing LVQA and long-form AI systems.

Problem

• Challenges in temporal reasoning and multimodal data processing in LVQA.
• Difficulty in retrieving sparse, cross-modal information from long videos.
• Need for efficient, scalable frameworks for long video understanding.

Innovation

• Unified textual representation for multimodal data
• Temporally aligned segments with adaptive filtering
• Vector database for precise content retrieval