# Multimodal Feature Fusion for Video Advertisements Tagging Via Stacking Ensemble

arXiv: https://arxiv.org/abs/2108.00679 · HTML: https://mlsanity.com/paper/2108.00679/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 2 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [Deep Image Retrieval: Learning global representations for image search](https://mlsanity.com/paper/1604.01325/) (2016)
- [Multimodal Machine Learning for Automated ICD Coding](https://mlsanity.com/paper/1810.13348/) (2018)
- [Cross-Domain Image Retrieval with Attention Modeling](https://mlsanity.com/paper/1709.01784/) (2017)
- [Implicit Media Tagging and Affect Prediction from video of spontaneous facial expressions, recorded with depth camera](https://mlsanity.com/paper/1701.05248/) (2017)

## Built on by (most cited first, 3 of 3)

- [Variable Importance in High-Dimensional Settings Requires Grouping](https://mlsanity.com/paper/2312.10858/) (2023)
- [MMRNet: Improving Reliability for Multimodal Object Detection and Segmentation for Bin Picking via Multimodal Redundancy](https://mlsanity.com/paper/2210.10842/) (2022)
- [A low complexity contextual stacked ensemble-learning approach for pedestrian intent prediction](https://mlsanity.com/paper/2410.13039/) (2024)

## Abstract (arXiv)

Automated tagging of video advertisements has been a critical yet challenging problem, and it has drawn increasing interests in last years as its applications seem to be evident in many fields. Despite sustainable efforts have been made, the tagging task is still suffered from several challenges, such as, efficiently feature fusion approach is desirable, but under-explored in previous studies. In this paper, we present our approach for Multimodal Video Ads Tagging in the 2021 Tencent Advertising Algorithm Competition. Specifically, we propose a novel multi-modal feature fusion framework, with the goal to combine complementary information from multiple modalities. This framework introduces stacking-based ensembling approach to reduce the influence of varying levels of noise and conflicts between different modalities. Thus, our framework can boost the performance of the tagging task, compared to previous methods. To empirically investigate the effectiveness and robustness of the proposed framework, we conduct extensive experiments on the challenge datasets. The obtained results suggest that our framework can significantly outperform related approaches and our method ranks as the 1st place on the final leaderboard, with a Global Average Precision (GAP) of 82.63%. To better promote the research in this field, we will release our code in the final version.