Name
Abstract | ||
|---|---|---|
Assembly state detection, i.e., object state detection, has a critical meaning in computer vision tasks, especially in AR assisted assembly. Unlike other object detection problems, the visual difference between different object states can be subtle. For the better learning of such subtle appearance difference, we proposed a two-level group attention module (TGA), which consists of inter-group attention and intro-group attention. The relationship between feature groups as well as the representation within each feature group is simultaneously enhanced. We embedded the proposed TGA module in a popular object detector and evaluated it on two new datasets related to object state estimation. The result shows that our proposed attention module outperforms the baseline attention module. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ISMAR-Adjunct51615.2020.00074 | 2020 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) |
Keywords | DocType | ISBN |
Computing methodologies,Machine learning,Machine learning approaches,Neural networks,Computing methodologies,Artificial intelligence,Computer vision,Computer vision tasks,Human-centered computing,Human computer interaction (HCI),Interaction paradigms,Mixed/ augmented reality | Conference | 978-1-7281-7676-5 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hangfan Liu | 1 | 0 | 0.34 |
| Yongzhi Su | 2 | 2 | 1.41 |
| Jason R. Rambach | 3 | 0 | 0.34 |
| Alain Pagani | 4 | 40 | 7.27 |
| Didier Stricker | 5 | 1266 | 138.03 |