Exploring the Role of Explicit Temporal Modeling in Multimodal Large Language Models for Video Understanding

• URL: http://arxiv.org/abs/2501.16786v1
• Date: Tue, 28 Jan 2025 08:30:58 GMT
• Title: Exploring the Role of Explicit Temporal Modeling in Multimodal Large Language Models for Video Understanding
• Authors: Yun Li, Zhe Liu, Yajing Kong, Guangrui Li, Jiyuan Zhang, Chao Bian, Feng Liu, Lina Yao, Zhenbang Sun,
• Abstract summary: Existing approaches adopt either implicit temporal modeling, relying solely on the decoder, or explicit temporal modeling, employing auxiliary temporal encoders. We propose the explicit Temporal (STE) to enable flexible explicit temporal modeling with adjustable receptive temporal fields and token compression ratios. Our findings emphasize the critical role of explicit temporal modeling, providing actionable insights to advance video MLLMs.
• Score: 23.477954901326978
• License:
• Abstract: Applying Multimodal Large Language Models (MLLMs) to video understanding presents significant challenges due to the need to model temporal relations across frames. Existing approaches adopt either implicit temporal modeling, relying solely on the LLM decoder, or explicit temporal modeling, employing auxiliary temporal encoders. To investigate this debate between the two paradigms, we propose the Stackable Temporal Encoder (STE). STE enables flexible explicit temporal modeling with adjustable temporal receptive fields and token compression ratios. Using STE, we systematically compare implicit and explicit temporal modeling across dimensions such as overall performance, token compression effectiveness, and temporal-specific understanding. We also explore STE's design considerations and broader impacts as a plug-in module and in image modalities. Our findings emphasize the critical role of explicit temporal modeling, providing actionable insights to advance video MLLMs.

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