Hierarchical Temporal Multi-Instance Learning for Video-based Student Learning Engagement Assessment
Authors: Jiayao Ma, Xinbo Jiang, Songhua Xu, Xueying Qin
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To verify the effectiveness of our method, we compare the performance of the proposed approach with that of several state-of-the-art peer solutions through extensive experiments. |
| Researcher Affiliation | Academia | Jiayao Ma1,2,3 , Xinbo Jiang1,2,3 , Songhua Xu4 , Xueying Qin1,2,3 1School of Software, Shandong University, Jinan, China 2Engineering Research Center of Digital Media Technology, Ministry of Education, China 3Key Laboratory of Shandong Province for Software Engineering, China 4College of Engineering and Computing, University of South Carolina, Columbia, SC, USA |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found. |
| Open Source Code | No | The paper does not state that its own source code is open-source or provide a link to it. |
| Open Datasets | No | Due to the lack of annotated long video data sets publicly available for our study on learning engagement assessment, we collect and annotate one in-house data set about online course studies. |
| Dataset Splits | No | And we randomly select three quarters of videos in the OCS data set as our train set and the remaining quarter as test set. |
| Hardware Specification | Yes | We establish the project based on Pytorch and train it on Tesla K80G GPU. |
| Software Dependencies | No | The paper mentions 'Pytorch' but does not specify its version number or other software dependencies with versions. |
| Experiment Setup | Yes | Our training epoch is set to 200. We initialize the learning rate to 0.001, and use Adam with a momentum of 0.9 and a weight decay of 1e-4 as the optimizer. When the training epoch is 60/100/160, we multiply the learning rate by 0.1. |