Summary of Hcvp: Leveraging Hierarchical Contrastive Visual Prompt For Domain Generalization, by Guanglin Zhou and Zhongyi Han and Shiming Chen and Biwei Huang and Liming Zhu and Tongliang Liu and Lina Yao and Kun Zhang
HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization
by Guanglin Zhou, Zhongyi Han, Shiming Chen, Biwei Huang, Liming Zhu, Tongliang Liu, Lina Yao, Kun Zhang
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed novel method for Domain Generalization (DG) aims to enhance the learning of domain-invariant features by supplementing the model with domain-level and task-specific characteristics. This approach, called Hierarchical Contrastive Visual Prompt (HCVP), utilizes a hierarchical prompt generation network enhanced by prompt contrastive learning to generate instance-dependent prompts that cater to unique characteristics in different domains and tasks. The HCVP methodology is designed to guide the model in separating invariant features from specific characteristics, thereby boosting generalization performance. The paper presents experiments on five DG datasets, demonstrating the effectiveness of HCVP in outperforming established DG algorithms and adaptation protocols. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help machine learning models learn about different types of data is introduced. This method is called Hierarchical Contrastive Visual Prompt (HCVP). It creates special pictures that are tailored to each type of data and task, allowing the model to better understand what’s unique about each one. This helps the model become more general and work well with new, unseen data. The paper shows that this approach works better than other methods for domain generalization. |
Keywords
» Artificial intelligence » Boosting » Domain generalization » Generalization » Machine learning » Prompt
