Summary of Promoting Ai Equity in Science: Generalized Domain Prompt Learning For Accessible Vlm Research, by Qinglong Cao et al.
Promoting AI Equity in Science: Generalized Domain Prompt Learning for Accessible VLM Research
by Qinglong Cao, Yuntian Chen, Lu Lu, Hao Sun, Zhenzhong Zeng, Xiaokang Yang, Dongxiao Zhang
First submitted to arxiv on: 14 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Applications (stat.AP)
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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 proposes the Generalized Domain Prompt Learning (GDPL) framework to facilitate the transfer of Vision-Language Models’ (VLMs) robust recognition capabilities from natural vision to specialized domains without requiring extensive data or resources. GDPL leverages small-scale domain-specific foundation models and minimal prompt samples, using quaternion networks to empower the language branch with domain knowledge and hierarchical propagation of generated vision prompt features to guide the vision branch into specific domains. The framework is tested across diverse domains like remote sensing, medical imaging, geology, Synthetic Aperture Radar, and fluid dynamics, achieving state-of-the-art domain recognition performance in a prompt learning paradigm. This breakthrough enables sustainable and inclusive VLM research, bridging the gap between academia and industry. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers have made great progress with computer models that can understand images and text together (Vision-Language Models or VLMs). These models are really good at recognizing things in everyday scenes. However, they require a lot of data, powerful computers, and energy to train. This makes it hard for researchers in universities to create their own specialized VLMs. To solve this problem, scientists have developed a new approach called Generalized Domain Prompt Learning (GDPL). GDPL helps transfer the knowledge from these computer models to specific domains like medical imaging or remote sensing without needing lots of data or powerful computers. This breakthrough makes it possible for researchers in universities and industry alike to create their own specialized VLMs, leading to more innovative discoveries. |
Keywords
» Artificial intelligence » Prompt
