From Image to Video, what do we need in multimodal LLMs?

• URL: http://arxiv.org/abs/2404.11865v1
• Date: Thu, 18 Apr 2024 02:43:37 GMT
• Title: From Image to Video, what do we need in multimodal LLMs?
• Authors: Suyuan Huang, Haoxin Zhang, Yan Gao, Yao Hu, Zengchang Qin,
• Abstract summary: Multimodal Large Language Models (MLLMs) have demonstrated profound capabilities in understanding multimodal information. We propose RED-VILLM, a Resource-Efficient Development pipeline for Video LLMs from Image LLMs. Our approach highlights the potential for a more cost-effective and scalable advancement in multimodal models.
• Score: 19.85928004619801
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal Large Language Models (MLLMs) have demonstrated profound capabilities in understanding multimodal information, covering from Image LLMs to the more complex Video LLMs. Numerous studies have illustrated their exceptional cross-modal comprehension. Recently, integrating video foundation models with large language models to build a comprehensive video understanding system has been proposed to overcome the limitations of specific pre-defined vision tasks. However, the current advancements in Video LLMs tend to overlook the foundational contributions of Image LLMs, often opting for more complicated structures and a wide variety of multimodal data for pre-training. This approach significantly increases the costs associated with these methods.In response to these challenges, this work introduces an efficient method that strategically leverages the priors of Image LLMs, facilitating a resource-efficient transition from Image to Video LLMs. We propose RED-VILLM, a Resource-Efficient Development pipeline for Video LLMs from Image LLMs, which utilizes a temporal adaptation plug-and-play structure within the image fusion module of Image LLMs. This adaptation extends their understanding capabilities to include temporal information, enabling the development of Video LLMs that not only surpass baseline performances but also do so with minimal instructional data and training resources. Our approach highlights the potential for a more cost-effective and scalable advancement in multimodal models, effectively building upon the foundational work of Image LLMs.

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