@inproceedings{raja-etal-2025-dataset,
    title = "A Dataset for Programming-based Instructional Video Classification and Question Answering",
    author = "Raja, Sana Javaid  and
      Zafar, Adeel  and
      Shoaib, Aqsa",
    editor = "Zhang, Wei Emma  and
      Dai, Xiang  and
      Elliot, Desmond  and
      Fang, Byron  and
      Sim, Mongyuan  and
      Zhuang, Haojie  and
      Chen, Weitong",
    booktitle = "Proceedings of the First Workshop of Evaluation of Multi-Modal Generation",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2025.evalmg-1.1/",
    pages = "1--9",
    abstract = "This work aims to develop an understanding of the rapidly emerging field of VideoQA, particularly in the context of instructional programming videos. It also encourages designing of system that can produce visual answer to programming based natural language questions. We introduce two datasets: CodeVidQA, with 2,104 question-answer pair links with timestamps taken from programming videos of Stack Overflow for Programming Visual Answer Localization task, and CodeVidCL with 4,331 videos (1,751 programming ,2580 non-programming) for Programming Video Classification task. In addition, we proposed a framework that adapts BigBird and SVM for video classification techniques. The proposed approach achieves a significantly high accuracy of 99.61{\%} for video classification."
}