i3d_nvgesture.md

MTUT (CVPR'2019)

@InProceedings{Abavisani_2019_CVPR,
  author = {Abavisani, Mahdi and Joze, Hamid Reza Vaezi and Patel, Vishal M.},
  title = {Improving the Performance of Unimodal Dynamic Hand-Gesture Recognition With Multimodal Training},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2019}
}

I3D (CVPR'2017)

@InProceedings{Carreira_2017_CVPR,
  author = {Carreira, Joao and Zisserman, Andrew},
  title = {Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {July},
  year = {2017}
}

NVGesture (CVPR'2016)

@InProceedings{Molchanov_2016_CVPR,
  author = {Molchanov, Pavlo and Yang, Xiaodong and Gupta, Shalini and Kim, Kihwan and Tyree, Stephen and Kautz, Jan},
  title = {Online Detection and Classification of Dynamic Hand Gestures With Recurrent 3D Convolutional Neural Network},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2016}
}

Results on NVGesture test set

Arch	Input Size	fps	bbox	AP_rgb	AP_depth	ckpt	log
I3D+MTUT*	112x112	15	$\surd$	0.725	0.730	ckpt	log
I3D+MTUT	224x224	30	$\surd$	0.782	0.811	ckpt	log
I3D+MTUT	224x224	30	$\times$	0.739	0.809	ckpt	log

^*: MTUT supports multi-modal training and uni-modal testing. Model trained with this config can be used to recognize gestures in rgb videos with inference config.