Summary of Hassod: Hierarchical Adaptive Self-supervised Object Detection, by Shengcao Cao et al.
HASSOD: Hierarchical Adaptive Self-Supervised Object Detection
by Shengcao Cao, Dhiraj Joshi, Liang-Yan Gui, Yu-Xiong Wang
First submitted to arxiv on: 5 Feb 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 paper proposes a novel approach to object detection, called Hierarchical Adaptive Self-Supervised Object Detection (HASSOD), which learns to detect objects and understand their compositions without human supervision. HASSOD uses a hierarchical adaptive clustering strategy to group regions into object masks based on self-supervised visual representations, adaptively determining the number of objects per image. The approach also identifies the hierarchical levels of objects in terms of composition by analyzing coverage relations between masks and constructing tree structures. This additional learning task leads to improved detection performance and enhanced interpretability. The paper also abandons the inefficient multi-round self-training process used in prior methods, instead adapting the Mean Teacher framework from semi-supervised learning, which results in a smoother and more efficient training process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to teach computers to detect objects in images without being shown what they are. It’s like how humans can learn to recognize objects just by looking at them. The new approach, called HASSOD, uses a special kind of grouping to help the computer understand the relationships between different parts of an object. This makes it better at detecting objects and understanding what they are made of. It also helps make the process more efficient and easier to understand. |
Keywords
* Artificial intelligence * Clustering * Object detection * Self supervised * Self training * Semi supervised
