Summary of Beyond Specialization: Assessing the Capabilities Of Mllms in Age and Gender Estimation, by Maksim Kuprashevich et al.
Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation
by Maksim Kuprashevich, Grigorii Alekseenko, Irina Tolstykh
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper compares the capabilities of powerful Multimodal Large Language Models (MLLMs) such as ShareGPT4V, ChatGPT, LLaVA-Next to a state-of-the-art specialized model called MiVOLO in the task of age and gender estimation. The results show interesting insights into the strengths and weaknesses of these models. The study also explores ways to fine-tune ShareGPT4V for this specific task, aiming to achieve state-of-the-art results. Although such a model is impractical for production due to its high cost, it could be useful in tasks like data annotation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper compares different computer models that can understand and work with various types of information, including text, images, and more. The goal is to see which one works best at guessing a person’s age and gender based on their name or description. The results show some surprising strengths and weaknesses of these powerful models. The study also tries to make the ShareGPT4V model better for this specific task, but realizes it wouldn’t be practical to use in real life due to its high cost. |
