Summary of Humanvlm: Foundation For Human-scene Vision-language Model, by Dawei Dai et al.
HumanVLM: Foundation for Human-Scene Vision-Language Model
by Dawei Dai, Xu Long, Li Yutang, Zhang Yuanhui, Shuyin Xia
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multimedia (cs.MM)
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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 The paper introduces a domain-specific large vision-language model called Human-Scene Vision-Language Model (HumanVLM) designed for human-scene vision-language understanding tasks. This is achieved by creating a large-scale multimodal image-text dataset sourced from the Internet, developing a captioning approach for human-centered images, and training a HumanVLM using this data. The paper evaluates HumanVLM across various downstream tasks, demonstrating superior performance among comparable models, particularly in human-related tasks. The model’s superiority is also demonstrated through its ability to outperform similar models such as Qwen2VL and ChatGPT-4o. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new AI model that can understand images with people in them better than other models. They created a big dataset of images and captions from the Internet, trained an AI model on this data, and tested it on different tasks. The results show that their model is better at understanding images with people than other similar models. |
Keywords
» Artificial intelligence » Language model » Language understanding
