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Local Temporal Bilinear Pooling for Fine-grained Action Parsing

2019

Conference Paper

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Fine-grained temporal action parsing is important in many applications, such as daily activity understanding, human motion analysis, surgical robotics and others requiring subtle and precise operations in a long-term period. In this paper we propose a novel bilinear pooling operation, which is used in intermediate layers of a temporal convolutional encoder-decoder net. In contrast to other work, our proposed bilinear pooling is learnable and hence can capture more complex local statistics than the conventional counterpart. In addition, we introduce exact lower-dimension representations of our bilinear forms, so that the dimensionality is reduced with neither information loss nor extra computation. We perform intensive experiments to quantitatively analyze our model and show the superior performances to other state-of-the-art work on various datasets.

Author(s): Yan Zhang and Siyu Tang and Krikamol Muandet and Christian Jarvers and Heiko Neumann
Book Title: Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)
Year: 2019
Month: June

Department(s): Empirical Inference, Perceiving Systems
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

Event Name: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2019
Event Place: Long Beach, USA

URL: https://arxiv.org/abs/1812.01922
Attachments: video demo

BibTex

@inproceedings{zhangbilinear2018,
  title = {Local Temporal Bilinear Pooling for Fine-grained Action Parsing},
  author = {Zhang, Yan and Tang, Siyu and Muandet, Krikamol and Jarvers, Christian and Neumann, Heiko},
  booktitle = {Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
  month = jun,
  year = {2019},
  url = {https://arxiv.org/abs/1812.01922},
  month_numeric = {6}
}