TGA: Two-level Group Attention for Assembly State Detection

Hangfan Liu, Yongzhi Su, Jason Raphael Rambach, Alain Pagani, Didier Stricker

In: Proceedings of the 19th IEEE ISMAR. IEEE International Symposium on Mixed and Augmented Reality (ISMAR-2020) November 9-13 Recife/Porto de Galinhas Brazil IEEE 2020.


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 a 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.

TGA_ISMAR2020.pdf (pdf, 10 MB)

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz