TV-RAG: A Temporal-aware and Semantic Entropy-Weighted Framework for Long Video Retrieval and Understanding

2025-12-29

AI Summary

To address the limitations of large video-language models (LVLMs) in long-video understanding—namely, narrow temporal context windows and insufficient modeling of cross-modal temporal dependencies—this paper proposes TV-RAG, a training-free framework. Methodologically, it introduces (1) a time-decay retrieval module that explicitly models cross-modal temporal alignment, and (2) a semantic entropy-weighted keyframe sampler that optimizes temporal sampling based on information density. These components jointly enable dual-level temporal-semantic reasoning, allowing plug-and-play, zero-shot adaptation to arbitrary LVLMs without fine-tuning. Evaluated on Video-MME, MLVU, and LongVideoBench, TV-RAG consistently outperforms state-of-the-art baselines across long-video question answering, temporal localization, and summarization tasks. It achieves significant performance gains while maintaining computational efficiency and lightweight deployment.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Large Vision Models
• Natural Language Processing: (Large) Language Models

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
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Large Video Language Models (LVLMs) have rapidly emerged as the focus of multimedia AI research. Nonetheless, when confronted with lengthy videos, these models struggle: their temporal windows are narrow, and they fail to notice fine-grained semantic shifts that unfold over extended durations. Moreover, mainstream text-based retrieval pipelines, which rely chiefly on surface-level lexical overlap, ignore the rich temporal interdependence among visual, audio, and subtitle channels. To mitigate these limitations, we propose TV-RAG, a training-free architecture that couples temporal alignment with entropy-guided semantics to improve long-video reasoning. The framework contributes two main mechanisms: emph{(i)} a time-decay retrieval module that injects explicit temporal offsets into the similarity computation, thereby ranking text queries according to their true multimedia context; and emph{(ii)} an entropy-weighted key-frame sampler that selects evenly spaced, information-dense frames, reducing redundancy while preserving representativeness. By weaving these temporal and semantic signals together, TV-RAG realises a dual-level reasoning routine that can be grafted onto any LVLM without re-training or fine-tuning. The resulting system offers a lightweight, budget-friendly upgrade path and consistently surpasses most leading baselines across established long-video benchmarks such as Video-MME, MLVU, and LongVideoBench, confirming the effectiveness of our model. The code can be found at https://github.com/AI-Researcher-Team/TV-RAG.

Problem

• Addresses narrow temporal windows in long video understanding models
• Overcomes surface-level lexical overlap in video retrieval systems
• Captures fine-grained semantic shifts across extended video durations

Innovation

• Time-decay retrieval module with explicit temporal offsets
• Entropy-weighted key-frame sampler for information-dense frames
• Training-free architecture combining temporal and semantic signals