# Video Ads Content Structuring by Combining Scene Confidence Prediction and Tagging

arXiv: https://arxiv.org/abs/2108.09215 · HTML: https://mlsanity.com/paper/2108.09215/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 12 references that are in the map.
- First posted on arXiv 20 Aug 2021.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [BMN: Boundary-Matching Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/1907.09702/) (2019)
- [YouTube-8M: A Large-Scale Video Classification Benchmark](https://mlsanity.com/paper/1609.08675/) (2016)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [PP-OCR: A Practical Ultra Lightweight OCR System](https://mlsanity.com/paper/2009.09941/) (2020)
- [A Local-to-Global Approach to Multi-modal Movie Scene Segmentation](https://mlsanity.com/paper/2004.02678/) (2020)

## Abstract (arXiv)

Video ads segmentation and tagging is a challenging task due to two main reasons: (1) the video scene structure is complex and (2) it includes multiple modalities (e.g., visual, audio, text.). While previous work focuses mostly on activity videos (e.g. "cooking", "sports"), it is not clear how they can be leveraged to tackle the task of video ads content structuring. In this paper, we propose a two-stage method that first provides the boundaries of the scenes, and then combines a confidence score for each segmented scene and the tag classes predicted for that scene. We provide extensive experimental results on the network architectures and modalities used for the proposed method. Our combined method improves the previous baselines on the challenging "Tencent Advertisement Video" dataset.