Name
Abstract | ||
|---|---|---|
In this demonstration, we present Exquisitor, a media explorer capable of learning user preferences in real-time during interactions with the 99.2 million images of YFCC100M. Exquisitor owes its efficiency to innovations in data representation, compression, and indexing. Exquisitor can complete each interaction round, including learning preferences and presenting the most relevant results, in less than 30 ms using only a single CPU core and modest RAM. In short, Exquisitor can bring large-scale interactive learning to standard desktops and laptops, and even high-end mobile devices.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3343031.3350580 | Proceedings of the 27th ACM International Conference on Multimedia |
Keywords | Field | DocType |
100 million images, interactive multimodal learning, scalability | Computer science,Multimedia | Conference |
ISBN | Citations | PageRank |
978-1-4503-6889-6 | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hanna Ragnarsdóttir | 1 | 0 | 0.68 |
| Þórhildur Þorleiksdóttir | 2 | 0 | 0.34 |
| Omar Shahbaz Khan | 3 | 0 | 3.38 |
| Björn Thór Jónsson | 4 | 1 | 4.75 |
| Gylfi Þór Guðmundsson | 5 | 4 | 1.11 |
| Jan Zahálka | 6 | 34 | 8.80 |
| Stevan Rudinac | 7 | 188 | 20.45 |
| Laurent Amsaleg | 8 | 1088 | 123.71 |
| Marcel Worring | 9 | 6439 | 384.88 |