Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | DocType |
|---|---|---|
2018 | AICS | Conference |
Volume | Citations | PageRank |
abs/1811.04115 | 1 | 0.43 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Murhaf Hossari | 1 | 1 | 1.11 |
| Soumyabrata Dev | 2 | 62 | 13.94 |
| Matthew Nicholson | 3 | 1 | 2.12 |
| Killian McCabe | 4 | 1 | 1.11 |
| Atul Nautiyal | 5 | 1 | 1.11 |
| Clare Conran | 6 | 1 | 2.12 |
| Jian Tang | 7 | 1 | 1.11 |
| Wei Xu | 8 | 1 | 1.11 |
| François Pitié | 9 | 237 | 15.59 |