Rethinking the Impacts of Overfitting and Feature Quality on Small-scale Video Classification
Xuansheng Wu, Feichi Yang, Tong Zhou, Xinyue Lin · 2021
While Transformers have yielded impressive results for video classification on large datasets recently, simpler models without the transformer architecture can be promising for small datasets. In this paper, we propose three major techniques to improve feature quality and another three to alleviate overfitting in an attempt to make lightweight models achieve higher performances. In particular, we enhance features of Image Flow by combining temporal information, multi-level features of CNNs, and Text embedding. We alleviate overfitting by removing redundant modal, fine-tuning dropout rate, and augmenting data. In the 2021 Tencent Advertisement Algorithm Competition, the baseline model achieved a GAP score of 0.8019 offline with our strategies. It's worth mentioning that our design works well with the 10-fold method, which produces our final submitting model with a GAP score of 0.8210 online, ranking the 5th among 287 teams. In addition, our solution is among the fastest within the top 10 teams.