CamouFinder: Finding Camouflaged Instances in Images
Abstract
In this paper, we investigate the interesting yet challenging problem of camouflaged instance segmentation. To this end, we first annotate the available CAMO dataset at the instance level. We also embed the data augmentation in order to increase the number of training samples. Then, we train different state-of-the-art instance segmentation on the CAMO-instance data. Last but not least, we develop an interactive user interface which demonstrates the performance of different state-of-the-art instance segmentation methods on the task of camouflaged instance segmentation. The users are able to compare the results of different methods on the given input images. Our work is expected to push the envelope of the camouflage analysis problem.
Cite
Text
Le et al. "CamouFinder: Finding Camouflaged Instances in Images." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I18.18015Markdown
[Le et al. "CamouFinder: Finding Camouflaged Instances in Images." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/le2021aaai-camoufinder/) doi:10.1609/AAAI.V35I18.18015BibTeX
@inproceedings{le2021aaai-camoufinder,
title = {{CamouFinder: Finding Camouflaged Instances in Images}},
author = {Le, Trung-Nghia and Nguyen, Vuong and Le, Cong and Nguyen, Tan-Cong and Tran, Minh-Triet and Nguyen, Tam V.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2021},
pages = {16071-16074},
doi = {10.1609/AAAI.V35I18.18015},
url = {https://mlanthology.org/aaai/2021/le2021aaai-camoufinder/}
}