Summary of Universal Domain Adaptive Object Detection Via Dual Probabilistic Alignment, by Yuanfan Zheng et al.
Universal Domain Adaptive Object Detection via Dual Probabilistic Alignment
by Yuanfan Zheng, Jinlin Wu, Wuyang Li, Zhen Chen
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper presents a novel framework called Dual Probabilistic Alignment (DPA) for domain adaptive object detection. The DPA is designed to handle open-set, partial-set, and closed-set domain adaptation by modeling domain probability as Gaussian distribution. This allows for heterogeneity domain distribution sampling and measurement. The DPA consists of three modules: Global-level Domain Private Alignment (GDPA), Instance-level Domain Shared Alignment (IDSA), and Private Class Constraint (PCC). The paper demonstrates the effectiveness of the proposed framework through extensive experiments on various datasets and scenarios, outperforming state-of-the-art UniDAOD and DAOD methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help machines learn from different sources of data. Right now, machines are great at learning from one type of data, but they struggle when the data looks very different. This new method, called Dual Probabilistic Alignment (DPA), helps machines learn from many types of data by looking at how likely it is that certain things appear in each dataset. The DPA has three parts: one for big-picture learning, one for detailed learning, and one to make sure the machine doesn’t get confused. This new method works really well on lots of different datasets and shows a lot of promise. |
Keywords
» Artificial intelligence » Alignment » Domain adaptation » Object detection » Probability
