Fine-Grained Similarity Measurement between Educational Videos and Exercises

By Xin Wang, Wei Huang, Qi Liu, Yu Yin, Zhenya Huang, Le Wu, Jianhui Ma, Xue Wang. 2020

20 citations recorded by the source corpus.

In online learning systems, measuring the similarity between educational videos and exercises is a fundamental task with great application potentials. In this paper, we explore to measure the fine-grained similarity by leveraging multimodal information. The problem remains pretty much open due to several domain-specific characteristics. First, unlike general videos, educational videos contain not only graphics but also text and formulas, which have a fixed reading order. Both spatial and temporal information embedded in the frames should be modeled. Second, there are semantic associations between adjacent video segments. The semantic associations will affect the similarity and different exercises usually focus on the related context of different ranges. Third, the fine-grained labeled data for training the model is scarce and costly. To tackle the aforementioned challenges, we propose VENet to measure the similarity at both video-level and segment-level by just exploiting the video-level labeled data. Extensive experimental results on real-world data demonstrate the effectiveness of VENet.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Class Gradient Projection For Continual Learning (2022, background): “Although the modern deep learning network (DNN) has shown good performance on the task at hand [5, 12, 24, 41, 46, 47], they always violently forget the previous knowledge when they learn new tasks.”