Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers

1 code implementation • CVPR 2019 • Zhen He, Jian Li, Daxue Liu, Hangen He, David Barber

To achieve both label-free and end-to-end learning of MOT, we propose a Tracking-by-Animation framework, where a differentiable neural model first tracks objects from input frames and then animates these objects into reconstructed frames.

Multi-Object Tracking Online Multi-Object Tracking

Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning

no code implementations • NeurIPS 2017 • Zhen He, Shao-Bing Gao, Liang Xiao, Daxue Liu, Hangen He, David Barber

The capacity of an LSTM network can be increased by widening and adding layers.