A Solution to Multi-modal Ads Video Tagging Challenge
Hao Wu, Jiajie Wang, Yuanzhe Gu, Peisen Zhao, Zhonglin Zu · 2021
In this paper, we present our solution to the Multi-modal Ads Video Tagging Challenge of Tencent Advertising Algorithm Competition in ACM Multimedia 2021 Grand Challenges. We extend the baseline model by redesigning the visual feature extraction procedure and we modify the loss function to cope with sparse positive targets. Moreover, we propose Semi-supervised Learning with Negative Masking to leverage both labeled data and unlabeled data from the preliminary contest which effectively enhances the training process. We further utilize Cross-Class Relevance Learning to boost the performance. We achieve 0.8237 GAP score via model ensemble and rank the second place among all submissions in the challenge.