Summary of Lifting Scheme-based Implicit Disentanglement Of Emotion-related Facial Dynamics in the Wild, by Xingjian Wang et al.
Lifting Scheme-Based Implicit Disentanglement of Emotion-Related Facial Dynamics in the Wild
by Xingjian Wang, Li Chai
First submitted to arxiv on: 17 Dec 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 This paper proposes a novel Implicit Facial Dynamics Disentanglement framework (IFDD) to address the challenge of recognizing emotion-related expressions in dynamic facial expression recognition (DFER). Most prior DFER methods use coupled spatiotemporal representations that may incorporate weakly relevant features with emotion-irrelevant context bias. IFDD disentangles emotion-related dynamic information from emotion-irrelevant global context in an implicit manner, without explicit guidance or operations. The framework consists of two stages: the Inter-frame Static-dynamic Splitting Module (ISSM) and the Lifting-based Aggregation-Disentanglement Module (LADM). ISSM splits frame features into two groups based on inter-frame correlation, while LADM aggregates and disentangles facial dynamic features from global context. Experimental results show that IFDD outperforms prior supervised DFER methods in terms of recognition accuracy and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to recognize emotions in faces by looking at how the face moves over time. Most previous attempts have mixed up important details with unimportant ones, making it hard to tell what’s really going on. The new method, called IFDD, gets around this problem by separating the important stuff from the unimportant stuff without needing extra help or instructions. It does this in two steps: first, it looks at each frame of video and separates the parts that change a lot (like the mouth) from the parts that don’t change much (like the forehead). Then, it uses these separated pieces to figure out what’s really happening on the face. The results show that IFDD is better than previous methods at recognizing emotions in faces. |
Keywords
» Artificial intelligence » Spatiotemporal » Supervised
