Flow-Event Autoencoder: Event Stream Object Recognition Dataset Generation with Arbitrary High Temporal Resolution
Abstract
Event camera has unique advantages in high temporal resolution and dynamic range and has shown potentials in several computer vision tasks. However, due to the novelty of this hardware, there’s a lack of large benchmark DVS event-stream datasets, including datasets for object recognition. In this work, we proposed an encoder-decoder method to augment event stream dataset from image and optical flow with arbitrary temporal resolution for object recognition task. We believe this proposed method can be generalized well in augmenting event stream vision data for object recognition and will help advance the development of event vision paradigm.
Cite
Text
Chen. "Flow-Event Autoencoder: Event Stream Object Recognition Dataset Generation with Arbitrary High Temporal Resolution." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30545Markdown
[Chen. "Flow-Event Autoencoder: Event Stream Object Recognition Dataset Generation with Arbitrary High Temporal Resolution." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/chen2024aaai-flow/) doi:10.1609/AAAI.V38I21.30545BibTeX
@inproceedings{chen2024aaai-flow,
title = {{Flow-Event Autoencoder: Event Stream Object Recognition Dataset Generation with Arbitrary High Temporal Resolution}},
author = {Chen, Minghai},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {23733-23735},
doi = {10.1609/AAAI.V38I21.30545},
url = {https://mlanthology.org/aaai/2024/chen2024aaai-flow/}
}