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Reading ability detection using eye-tracking data with LSTM-based few-shot learning

arXiv 2409.08798 ↗First posted 13 Sep 2024cs.HC cs.AIcited by 0

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Abstract (arXiv)

Reading ability detection is important in modern educational field. In this paper, a method of predicting scores of reading ability is proposed, using the eye-tracking data of a few subjects (e.g., 68 subjects). The proposed method built a regression model for the score prediction by combining Long Short Time Memory (LSTM) and light-weighted neural networks. Experiments show that with few-shot learning strategy, the proposed method achieved higher accuracy than previous methods of score prediction in reading ability detection. The code can later be downloaded at https://github.com/pumpkinLNX/LSTM-eye-tracking-pytorch.git

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 42 references that are in the map. First posted on arXiv 13 Sep 2024.

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References · 14

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2019Episodic Training for Domain Generalizationcited by 245
  3. 2022Learning from Few Examples: A Summary of Approaches to Few-Shot Learningcited by 86
  4. 2021Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learningcited by 49
  5. 2019Explainable artificial intelligence model to predict acute critical illness from electronic health recordscited by 42
  6. 2021Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentationcited by 33
  7. 2021Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Surveycited by 23
  8. 2021LibFewShot: A Comprehensive Library for Few-shot Learningcited by 21
  9. 2021Few-Shot Learning with Class Imbalancecited by 18
  10. 2022What Matters For Meta-Learning Vision Regression Tasks?cited by 18
  11. 2022Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overviewcited by 16
  12. 2022Eye-tracking based classification of Mandarin Chinese readers with and without dyslexia using neural sequence modelscited by 10
2 more
  1. 2019EEG-Based Driver Drowsiness Estimation Using Feature Weighted Episodic Trainingcited by 9
  2. 2023Are LSTMs Good Few-Shot Learners?cited by 3

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