Exploring a Mixed Representation for Encoding Temporal Coherence
Abstract
Guiding representation learning towards temporally stable features improves object identity encoding from video. Existing models have applied temporal coherence uniformly over all features based on the assumption that optimal object identity encoding only requires temporally stable components. We explore the effects of mixing temporally coherent invariant features alongside variable features in a single representation. Applying temporal coherence to different proportions of available features, we introduce a mixed representation autoencoder. Trained on several datasets, model outputs were passed to an object classification task to compare performance. Whilst the inclusion of temporal coherence improved object identity recognition in all cases, the majority of tests favoured a mixed representation.
Cite
Text
Parkinson et al. "Exploring a Mixed Representation for Encoding Temporal Coherence." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016. doi:10.1007/978-3-319-46227-1_22Markdown
[Parkinson et al. "Exploring a Mixed Representation for Encoding Temporal Coherence." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016.](https://mlanthology.org/ecmlpkdd/2016/parkinson2016ecmlpkdd-exploring/) doi:10.1007/978-3-319-46227-1_22BibTeX
@inproceedings{parkinson2016ecmlpkdd-exploring,
title = {{Exploring a Mixed Representation for Encoding Temporal Coherence}},
author = {Parkinson, Jon and Sandouk, Ubai and Chen, Ke},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2016},
pages = {347-360},
doi = {10.1007/978-3-319-46227-1_22},
url = {https://mlanthology.org/ecmlpkdd/2016/parkinson2016ecmlpkdd-exploring/}
}