Despite recent advances in computer vision based on various convolutional architectures, video understanding remains an important challenge. In this work, we present and discuss a top solution for the large-scale video classification (labeling) problem introduced as a Kaggle competition based on the YouTube-8M dataset. We show and compare different approaches to preprocessing, data augmentation, model architectures, and model combination. Our final model is based on a large ensemble of video- and frame-level models but fits into rather limiting hardware constraints. We apply an approach based on knowledge distillation to deal with noisy labels in the original dataset and the recently developed mixup technique to improve the basic models.
翻译:尽管最近在基于各种连锁结构的计算机愿景方面有所进步,但视频理解仍然是一个重大挑战。在这项工作中,我们提出并讨论一个基于YouTube-8M数据集的大规模视频分类(标签)问题的顶尖解决方案。我们展示并比较了预处理、数据扩增、模型架构和模型组合的不同方法。我们的最后模型基于大量视频和框架级模型组合,但又适应了相当有限的硬件限制。我们采用了基于知识蒸馏的方法来处理原始数据集中的噪音标签和最近开发的混合技术来改进基本模型。