Huang, Q., & Chen, J. (2024). xLSTM-FER: Enhancing Student Expression Recognition with Extended Vision Long Short-Term Memory Network. arXiv preprint arXiv:2410.05074v1.
This paper introduces xLSTM-FER, a novel deep learning architecture for facial expression recognition (FER) in students, aiming to address the limitations of existing methods like CNNs and ViTs in capturing long-range dependencies and handling high-resolution images efficiently.
xLSTM-FER leverages an extended version of Long Short-Term Memory (xLSTM) networks. It segments input images into patches, encodes them using stacked xLSTM blocks to capture spatial-temporal dynamics, and employs a classification head for emotion prediction. The model utilizes a memory matrix within the mLSTM layer for enhanced parallel processing and incorporates a path transfer mechanism for a comprehensive image representation.
xLSTM-FER presents a promising approach for student FER, offering high accuracy and computational efficiency. Its ability to capture long-range dependencies and handle high-resolution images effectively addresses key limitations of existing methods.
This research significantly contributes to the field of computer vision, particularly in FER, by introducing a novel architecture that balances accuracy and efficiency. It has practical implications for educational technology, enabling more effective assessment of student engagement and emotional states.
While xLSTM-FER shows promising results, further research can explore its generalization capabilities across diverse ethnicities and lighting conditions. Investigating its integration with multimodal learning, incorporating audio and contextual information, could further enhance its robustness and accuracy in real-world educational settings.
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by Qionghao Hua... : arxiv.org 10-08-2024
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