# ADNet: A Deep Network for Detecting Adverts

arXiv: https://arxiv.org/abs/1811.04115 · HTML: https://mlsanity.com/paper/1811.04115/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2018.

## Built on by (most cited first, 2 of 2)

- [Stereo Matching Based on Visual Sensitive Information](https://mlsanity.com/paper/2105.10831/) (2021)
- [Frequency-centroid features for word recognition of non-native English speakers](https://mlsanity.com/paper/2206.07176/) (2022)

## Abstract (arXiv)

Online video advertising gives content providers the ability to deliver compelling content, reach a growing audience, and generate additional revenue from online media. Recently, advertising strategies are designed to look for original advert(s) in a video frame, and replacing them with new adverts. These strategies, popularly known as product placement or embedded marketing, greatly help the marketing agencies to reach out to a wider audience. However, in the existing literature, such detection of candidate frames in a video sequence for the purpose of advert integration, is done manually. In this paper, we propose a deep-learning architecture called ADNet, that automatically detects the presence of advertisements in video frames. Our approach is the first of its kind that automatically detects the presence of adverts in a video frame, and achieves state-of-the-art results on a public dataset.