Answering from Sure to Uncertain: Uncertainty-Aware Curriculum Learning for Video Question Answering


Haopeng Li (The University of Melbourne), Mohammed Bennamoun (University of Western Australia), Jun Liu (Lancaster University), Hossein Rahmani (Lancaster University), Qiuhong Ke (Monash University)
The 35th British Machine Vision Conference
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Abstract

While significant advancements have been made in video question answering (VideoQA), the potential benefits of enhancing model generalization through tailored difficulty scheduling have been largely overlooked in existing research. This paper seeks to bridge that gap by incorporating VideoQA into a curriculum learning (CL) framework that progressively trains models from simpler to more complex data. Recognizing that conventional self-paced CL methods rely on training loss for difficulty measurement, which might not accurately reflect the intricacies of video-question pairs, we introduce the concept of uncertainty-aware CL. Here, uncertainty serves as the guiding principle for dynamically adjusting the difficulty. Furthermore, we address the challenge posed by uncertainty by presenting a probabilistic modeling approach for VideoQA. Specifically, we conceptualize VideoQA as a stochastic computation graph, where the hidden representations are treated as stochastic variables. This yields two distinct types of uncertainty: one related to the inherent uncertainty in the data and another pertaining to the model's confidence. In practice, we seamlessly integrate the VideoQA model into our framework and conduct comprehensive experiments. The findings affirm that our approach not only achieves enhanced performance but also effectively quantifies uncertainty in the context of VideoQA.

Citation

@inproceedings{Li_2025_BMVC,
author    = {Haopeng Li and Mohammed Bennamoun and Jun Liu and Hossein Rahmani and Qiuhong Ke},
title     = {Answering from Sure to Uncertain: Uncertainty-Aware Curriculum Learning for Video Question Answering},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_444/paper.pdf}
}


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