Name
Abstract | ||
|---|---|---|
Action recognition is an important task for video understanding with broad applications. However, developing an effective action recognition solution often requires extensive engineering efforts in building and testing different combinations of the modules and their hyperparameters. In this demo, we present AutoVideo, a Python system for automated video action recognition. AutoVideo is featured for 1) highly modular and extendable infrastructure following the standard pipeline language, 2) an exhaustive list of primitives for pipeline construction, 3) data-driven tuners to save the efforts of pipeline tuning, and 4) easy-to-use Graphical User Interface (GUI). AutoVideo is released under MIT license at https://github.com/datamllab/autovideo |
Year | DOI | Venue |
|---|---|---|
2022 | 10.24963/ijcai.2022/862 | European Conference on Artificial Intelligence |
Keywords | DocType | Citations |
Machine Learning: Automated Machine Learning,Computer Vision: Video analysis and understanding | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 12 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daochen Zha | 1 | 16 | 8.13 |
| Zaid Pervaiz Bhat | 2 | 0 | 1.01 |
| Yi-Wei Chen | 3 | 0 | 1.01 |
| Yicheng Wang | 4 | 22 | 8.06 |
| Sirui Ding | 5 | 0 | 1.01 |
| Jiaben Chen | 6 | 0 | 0.34 |
| Kwei-Herng Lai | 7 | 0 | 0.34 |
| Mohammad Qazim Bhat | 8 | 0 | 1.01 |
| Anmoll Kumar Jain | 9 | 0 | 1.01 |
| Alfredo Costilla Reyes | 10 | 0 | 1.01 |
| Na Zou | 11 | 0 | 0.68 |
| Xia Hu | 12 | 0 | 0.34 |