S3D: Single Shot multi-Span Detector via Fully 3D Convolutional Network

TensorFlow implementation of the single shot activity detection model described in our BMVC 2018 oral paper S3D: Single Shot multi-Span Detector via Fully 3D Convolutional Network.

In this work, we present a novel single shot multi-span detector using a simple end-to-end fully Conv3D network. The network architecture is intrinsically simple and achieves state-of-the-art performance on THUMOS'14 detection benchmark, while maintaining fast runtime speed at 1271 FPS. For more details, please check the latest version of the paper.

Network architecture

Prerequisites

• Python 2.7
• TensorFlow 1.0

Resources

We are still working on it and will release the codes soon.

Reference

If you find our work useful, please use the following bibtex to cite our work:

@inproceedings{zhang2018s3d,
  author = {Zhang, Da and Dai, Xiyang and Wang, Xin and Wang, Yuan-Fang},
  title = {S3D: Single Shot multi-Span Detector via Fully 3D Convolutional Network},
  booktitle = {Proceedings of the British Machine Vision Conference (BMVC)},
  year = {2018}
}