Few-Shot Continual Learning for Activity Recognition in Classroom Surveillance Images
Project Overview
The document explores the innovative use of generative AI in education, particularly through the development of the ARIC dataset and a few-shot continual learning approach aimed at activity recognition within educational environments. It emphasizes the importance of accurately recognizing a range of activities, both teaching and non-teaching, from classroom surveillance images, while tackling challenges like class imbalance and the high similarity between different activities. The proposed method integrates supervised contrastive learning with an adaptive covariance classifier, significantly enhancing the model's performance in real-world classroom scenarios. The findings suggest that this approach not only improves activity recognition accuracy but also offers insights into classroom dynamics, thus contributing to a better understanding of educational environments and potentially informing teaching strategies and classroom management practices.
Key Applications
Few-Shot Continual Learning for Activity Recognition in Classroom Surveillance Images
Context: Educational context focusing on real classroom settings, targeting educators and researchers in AI and education.
Implementation: Developed a dataset (ARIC) and a few-shot continual learning method that integrates supervised contrastive learning and adaptive covariance classifier.
Outcomes: Enhanced model's generalization ability, improved accuracy in activity recognition, and better handling of class imbalance and high similarity among activities.
Challenges: Class imbalance, high similarity between activity types, privacy concerns, and continuous occurrence of non-instructional activities.
Implementation Barriers
Technical Barrier
Challenges in recognizing activities due to class imbalance and high similarity between activities.
Proposed Solutions: Utilizing few-shot continual learning methods to enhance the model's ability to learn from limited samples.
Privacy Barrier
Need to protect the privacy of individuals in classroom surveillance images while recognizing activities.
Proposed Solutions: Using shallow layers of pre-trained models to convert images into feature data, avoiding the release of original images.
Project Team
Yilei Qian
Researcher
Kanglei Geng
Researcher
Kailong Chen
Researcher
Shaoxu Cheng
Researcher
Linfeng Xu
Researcher
Hongliang Li
Researcher
Fanman Meng
Researcher
Qingbo Wu
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Yilei Qian, Kanglei Geng, Kailong Chen, Shaoxu Cheng, Linfeng Xu, Hongliang Li, Fanman Meng, Qingbo Wu
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai