Neural Graph Matching Networks for Fewshot 3D Action Recognition

Michelle Guo, Edward Chou, De-An Huang, Shuran Song, Serena Yeung, Li Fei-Fei

ECCV 2018

doi:10.1007/978-3-030-01246-5_40 /eccv/2018/guo2018eccv-neural/

Abstract

We propose Neural Graph Matching (NGM) Networks, a novel framework that can learn to recognize a previous unseen 3D action class with only a few examples. We achieve this by leveraging the inherent structure of 3D data through a graphical representation. This allows us to modularize our model and lead to strong data-efficiency in few-shot learning. More specifically, NGM Networks jointly learn a graph generator and a graph matching metric function in a end-to-end fashion to directly optimize the few-shot learning objective. We evaluate NGM on two 3D action recognition datasets, CAD-120 and PiGraphs, and show that learning to generate and match graphs both lead to significant improvement of few-shot 3D action recognition over the holistic baselines.

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Cite

Text

Guo et al. "Neural Graph Matching Networks for Fewshot 3D Action Recognition." Proceedings of the European Conference on Computer Vision (ECCV), 2018. doi:10.1007/978-3-030-01246-5_40

Markdown

[Guo et al. "Neural Graph Matching Networks for Fewshot 3D Action Recognition." Proceedings of the European Conference on Computer Vision (ECCV), 2018.](https://mlanthology.org/eccv/2018/guo2018eccv-neural/) doi:10.1007/978-3-030-01246-5_40

BibTeX

@inproceedings{guo2018eccv-neural,
  title     = {{Neural Graph Matching Networks for Fewshot 3D Action Recognition}},
  author    = {Guo, Michelle and Chou, Edward and Huang, De-An and Song, Shuran and Yeung, Serena and Fei-Fei, Li},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year      = {2018},
  doi       = {10.1007/978-3-030-01246-5_40},
  url       = {https://mlanthology.org/eccv/2018/guo2018eccv-neural/}
}