Summary of A Survey Of Deep Learning For Group-level Emotion Recognition, by Xiaohua Huang et al.
A Survey of Deep Learning for Group-level Emotion Recognition
by Xiaohua Huang, Jinke Xu, Wenming Zheng, Qirong Mao, Abhinav Dhall
First submitted to arxiv on: 13 Aug 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 The paper presents a comprehensive review of Deep Learning (DL) techniques applied to Group-Level Emotion Recognition (GER), a crucial area in analyzing human behavior. The authors propose a new taxonomy for the field, covering all aspects of GER based on DL methods. The survey provides an overview of datasets, the deep GER pipeline, and performance comparisons of state-of-the-art methods over the past decade. It also summarizes fundamental approaches and advanced developments for each aspect, highlighting outstanding challenges and suggesting potential avenues for designing robust GER systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using artificial intelligence to understand how groups of people are feeling. Right now, there are many ways to do this, but they mostly use old-fashioned methods that aren’t very good. The authors look at the latest techniques that use deep learning, which are really good at doing lots of tasks. They tell us what these techniques are and how they work. They also compare them to each other and talk about what’s working well and what needs to be improved. |
Keywords
» Artificial intelligence » Deep learning
