Summary of Hi-ef: Benchmarking Emotion Forecasting in Human-interaction, by Haoran Wang et al.
Hi-EF: Benchmarking Emotion Forecasting in Human-interaction
by Haoran Wang, Xinji Mai, Zeng Tao, Yan Wang, Jiawen Yu, Ziheng Zhou, Xuan Tong, Shaoqi Yan, Qing Zhao, Shuyong Gao, Wenqiang Zhang
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 proposes a novel approach to Affective Forecasting, which predicts an individual’s future emotions, by transforming it into a Deep Learning problem. The Emotion Forecasting (EF) task is designed based on two-party interactions, where the emotions of one person are influenced by the emotions or other information conveyed during interactions with another person. To tackle this task, the authors developed a specialized dataset called Human-interaction-based Emotion Forecasting (Hi-EF), which contains 3069 two-party Multilayered-Contextual Interaction Samples (MCIS) with abundant affective-relevant labels and three modalities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Affective forecasting is hard because it’s affected by things like what others think or how far away the event is. This paper makes it a Deep Learning problem, which helps predict people’s emotions better. They created a special dataset called Hi-EF with lots of examples of two people talking and feeling certain ways. This shows that it can be done and might be useful. |
Keywords
* Artificial intelligence * Deep learning
