Multiple Temporal Fusion based Weakly-supervised Pre-training Techniques for Video Categorization

In this paper, we present our solution of the ACM Multimedia 2022 pre-training for video understanding challenge. First, we pre-train the models on large-scale weakly-supervised video datasets with different temporal resolutions, then fine-tune the model for downstream application. Quantitative comparisons are conducted to evaluate the performance of different networks at multiple temporal resolutions. Moreover, we fusion different pre-trained models through weighted averaging. We achieve an accuracy of 62.39% in the testing set, which ranked as the first place in the video categorization track of this challenge.