Zhuolin Tan

Zhuolin Tan, Chenqiang Gao, Anyong Qin et al.

Analyzing student actions is an important and challenging task in educational research. Existing efforts have been hampered by the lack of accessible datasets to capture the nuanced action dynamics in classrooms. In this paper, we present a new multi-label Student Action Video (SAV) dataset, specifically designed for action detection in classroom settings. The SAV dataset consists of 4,324 carefully trimmed video clips from 758 different classrooms, annotated with 15 distinct student actions. Compared to existing action detection datasets, the SAV dataset stands out by providing a wide range of real classroom scenarios, high-quality video data, and unique challenges, including subtle movement differences, dense object engagement, significant scale differences, varied shooting angles, and visual occlusion. These complexities introduce new opportunities and challenges to advance action detection methods. To benchmark this, we propose a novel baseline method based on a visual transformer, designed to enhance attention to key local details within small and dense object regions. Our method demonstrates excellent performance with a mean Average Precision (mAP) of 67.9% and 27.4% on the SAV and AVA datasets, respectively. This paper not only provides the dataset but also calls for further research into AI-driven educational tools that may transform teaching methodologies and learning outcomes. The code and dataset are released at https://github.com/Ritatanz/SAV.

Anyong Qin, Zhaowei Shang, Zhuolin Tan et al.

Unsupervised dimensionality reduction is one of the commonly used techniques in the field of high dimensional data recognition problems. The deep autoencoder network which constrains the weights to be non-negative, can learn a low dimensional part-based representation of data. On the other hand, the inherent structure of the each data cluster can be described by the distribution of the intraclass samples. Then one hopes to learn a new low dimensional representation which can preserve the intrinsic structure embedded in the original high dimensional data space perfectly. In this paper, by preserving the data distribution, a deep part-based representation can be learned, and the novel algorithm is called Distribution Preserving Network Embedding (DPNE). In DPNE, we first need to estimate the distribution of the original high dimensional data using the $k$-nearest neighbor kernel density estimation, and then we seek a part-based representation which respects the above distribution. The experimental results on the real-world data sets show that the proposed algorithm has good performance in terms of cluster accuracy and AMI. It turns out that the manifold structure in the raw data can be well preserved in the low dimensional feature space.