Summary of A Review Of Human-object Interaction Detection, by Yuxiao Wang et al.
A Review of Human-Object Interaction Detection
by Yuxiao Wang, Yu Lei, Li Cui, Weiying Xue, Qi Liu, Zhenao Wei
First submitted to arxiv on: 20 Aug 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 paper systematically summarizes recent work in image-based Human-Object Interaction (HOI) detection, a crucial task for high-level visual understanding that facilitates comprehension of human activities. The success of this task relies on accurate localization of human and object instances, as well as correct classification of object categories and interaction relationships. The abstract discusses mainstream datasets involved in HOI relationship detection, including two-stage methods and end-to-end one-stage detection approaches, analyzing their strengths and weaknesses. It also explores advancements in zero-shot learning, weakly supervised learning, and the application of large-scale language models in HOI detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a way for computers to understand what’s happening in pictures or videos by detecting when people are interacting with objects. It’s an important task because it helps us understand human activities. The researchers looked at what datasets are commonly used, and then they discussed different methods that computers use to do this task. They also talked about how computers can learn without being shown examples of every situation, which is useful if we want the computer to be able to understand new situations. |
Keywords
* Artificial intelligence * Classification * Supervised * Zero shot
