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Automated tagging of video advertisements has been a critical yet challengingproblem, and it has drawn increasing interests in last years as itsapplications seem to be evident in many fields. Despite sustainable effortshave been made, the tagging task is still suffered from several challenges,such as, efficiently feature fusion approach is desirable, but under-exploredin previous studies. In this paper, we present our approach for MultimodalVideo Ads Tagging in the 2021 Tencent Advertising Algorithm Competition.Specifically, we propose a novel multi-modal feature fusion framework, with thegoal to combine complementary information from multiple modalities. Thisframework introduces stacking-based ensembling approach to reduce the influenceof varying levels of noise and conflicts between different modalities. Thus,our framework can boost the performance of the tagging task, compared toprevious methods. To empirically investigate the effectiveness and robustnessof the proposed framework, we conduct extensive experiments on the challengedatasets. The obtained results suggest that our framework can significantlyoutperform related approaches and our method ranks as the 1st place on thefinal leaderboard, with a Global Average Precision (GAP) of 82.63%. To betterpromote the research in this field, we will release our code in the finalversion.

Multimodal Feature Fusion for Video Advertisements Tagging Via Stacking Ensemble