Multimodal Large Language Models (MLLMs), especially those integrating language and vision modalities (LVMs), are revolutionizing various fields with their high accuracy, generalization capability, and robust performance. MiVOLOv2, a state-of-the-art model for gender and age determination, outperforms general-purpose MLLMs in age estimation. The research paper evaluates the potential of neural networks, including LLaVA and ShareGPT.
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Rapid Advancement of Multimodal Large Language Models (MLLMs)
The rapid development of MLLMs integrating language and vision modalities (LVMs) has revolutionized various fields, prompting a re-evaluation of specialized models. These models offer high accuracy, generalization capability, reasoning skills, and robust performance, making them experts in handling unforeseen tasks beyond their initial training scope.
Practical Solutions and Value
Specialized models like MiVOLO offer a cost-effective solution for tasks such as data annotation and object segmentation. MiVOLOv2, the state-of-the-art model for gender and age determination, outperforms all specialized models and general-purpose MLLMs. It utilizes advanced evaluation metrics and an extended training dataset to achieve superior performance.
Comparative Analysis and Practical Applications
The paper compares the best general-purpose MLLMs with technical models like MiVOLO, highlighting significant differences in computational costs and speed for various tasks. MiVOLOv2’s capabilities in age and gender estimation tasks surpass all general-purpose MLLMs, demonstrating its practical applications in image processing and recognition tasks.
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