Summary of Distilling Privileged Multimodal Information For Expression Recognition Using Optimal Transport, by Muhammad Haseeb Aslam et al.
Distilling Privileged Multimodal Information for Expression Recognition using Optimal Transport
by Muhammad Haseeb Aslam, Muhammad Osama Zeeshan, Soufiane Belharbi, Marco Pedersoli, Alessandro Koerich, Simon Bacon, Eric Granger
First submitted to arxiv on: 27 Jan 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 research proposes a novel approach to deep learning models for multimodal expression recognition, which has achieved remarkable performance in controlled environments but struggles in real-world applications due to limited availability of training data. The proposed method, privileged knowledge distillation (PKDOT), leverages point-to-point matching and captures structural information in the teacher representation space formed by introducing privileged modalities during training. PKDOT is demonstrated to outperform state-of-the-art privileged KD methods on two challenging problems: pain estimation on Biovid dataset and arousal-valance prediction on Affwild2 dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning models can recognize emotions and expressions from multiple sources, but these models struggle in real-life situations because they need a lot of data. Researchers have found a way to improve this by using more information during training, even if it’s not available at test time. This new approach, called PKDOT, is better than previous methods for recognizing pain and emotions. |
