A Comprehensive Review of Survey on Efficient Multimodal Large Language Models

Multimodal Large Language Models (MLLMs)

Multimodal large language models (MLLMs) are advanced AI innovations that combine language and vision capabilities to handle tasks like visual question answering & image captioning. These models integrate multiple data modalities to significantly enhance their performance across various applications, marking a substantial advancement in AI.

Resource Challenges

The main challenge with MLLMs is their extensive resource requirements, hindering widespread adoption. Training and inference costs are prohibitive for many organizations, limiting their use in resource-constrained environments like edge computing.

Efficiency Optimization

Efforts are focused on optimizing the efficiency of MLLMs by reducing model size and leveraging pre-training knowledge to save resources. Research has explored strategies to create efficient MLLMs, categorizing advancements into key areas like architecture, vision processing, and language model efficiency.

Innovative Techniques

Efficient MLLMs employ innovative techniques such as vision token compression and lightweight model structures to achieve notable improvements in computational efficiency without sacrificing performance.

Practical Applications

Efficient MLLMs have practical applications in document understanding and video comprehension, addressing the challenges of high-resolution image and video processing.

Real-world Applicability

The advancements in efficient MLLMs make it feasible for researchers and organizations to utilize these powerful models in real-world scenarios, such as edge computing and resource-limited environments.

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