docs/references/References.bib

% Encoding: UTF-8

% An Empirical Study of Training Self-Supervised Vision Transformers
@inproceedings{chen2021empirical,
title={An empirical study of training self-supervised vision transformers},
author={Chen, Xinlei and Xie, Saining and He, Kaiming},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={9640--9649},
year={2021}
}

% 2D positional embedding
@article{raisi20202d,
title={2D positional embedding-based transformer for scene text recognition},
author={Raisi, Zobeir and Naiel, Mohamed A and Fieguth, Paul and Wardell, Steven and Zelek, John},
journal={Journal of Computational Vision and Imaging Systems},
volume={6},
number={1},
pages={1--4},
year={2020}
}

% Layer Normalization
@article{ba2016layer,
title={Layer normalization},
author={Ba, Jimmy Lei and Kiros, Jamie Ryan and Hinton, Geoffrey E},
journal={arXiv preprint arXiv:1607.06450},
year={2016}
}

% Batch Normalization
@inproceedings{ioffe2015batch,
title={Batch normalization: Accelerating deep network training by reducing internal covariate shift},
author={Ioffe, Sergey and Szegedy, Christian},
booktitle={International conference on machine learning},
pages={448--456},
year={2015},
organization={PMLR}
}

% ReLU
@article{fukushima1975cognitron,
title={Cognitron: A self-organizing multilayered neural network},
author={Fukushima, Kunihiko},
journal={Biological cybernetics},
volume={20},
number={3},
pages={121--136},
year={1975},
publisher={Springer}
}

% Weight Normalization
@article{salimans2016weight,
title={Weight normalization: A simple reparameterization to accelerate training of deep neural networks},
author={Salimans, Tim and Kingma, Durk P},
journal={Advances in neural information processing systems},
volume={29},
year={2016}
}

% Stochastic depth
@inproceedings{huang2016deep,
title={Deep networks with stochastic depth},
author={Huang, Gao and Sun, Yu and Liu, Zhuang and Sedra, Daniel and Weinberger, Kilian Q},
booktitle={European conference on computer vision},
pages={646--661},
year={2016},
organization={Springer}
}

% Stereo Matching Algorithm
@article{zhong2020displacement,
title={Displacement-invariant cost computation for efficient stereo matching},
author={Zhong, Yiran and Loop, Charles and Byeon, Wonmin and Birchfield, Stan and Dai, Yuchao and Zhang, Kaihao and Kamenev, Alexey and Breuel, Thomas and Li, Hongdong and Kautz, Jan},
journal={arXiv preprint arXiv:2012.00899},
year={2020}
}

% wandb
@misc{wandb,
title = {Experiment Tracking with Weights and Biases},
year = {2020},
note = {Software available from wandb.com},
url={https://www.wandb.com/},
author = {Biewald, Lukas},
}

% 
@article{borji2015salient,
title={Salient object detection: A benchmark},
author={Borji, Ali and Cheng, Ming-Ming and Jiang, Huaizu and Li, Jia},
journal={IEEE transactions on image processing},
volume={24},
number={12},
pages={5706--5722},
year={2015},
publisher={IEEE}
}

% SOD metrics
@misc{sodmetrics,
title = {PySODMetrics: A simple and efficient implementation of SOD metrics},
howpublished = {\url{https://github.com/lartpang/PySODMetrics}},
note = {Accessed: 2022-10-31}
}

% MAE
@inproceedings{perazzi2012saliency,
title={Saliency filters: Contrast based filtering for salient region detection},
author={Perazzi, Federico and Kr{\"a}henb{\"u}hl, Philipp and Pritch, Yael and Hornung, Alexander},
booktitle={2012 IEEE conference on computer vision and pattern recognition},
pages={733--740},
year={2012},
organization={IEEE}
}

% F-measure
@inproceedings{achanta2009frequency,
title={Frequency-tuned salient region detection},
author={Achanta, Radhakrishna and Hemami, Sheila and Estrada, Francisco and Susstrunk, Sabine},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={1597--1604},
year={2009},
organization={IEEE}
}

% E-measure
@article{fan2018enhanced,
title={Enhanced-alignment measure for binary foreground map evaluation},
author={Fan, Deng-Ping and Gong, Cheng and Cao, Yang and Ren, Bo and Cheng, Ming-Ming and Borji, Ali},
journal={arXiv preprint arXiv:1805.10421},
year={2018}
}

% S-measure
@inproceedings{fan2017structure,
title={Structure-measure: A new way to evaluate foreground maps},
author={Fan, Deng-Ping and Cheng, Ming-Ming and Liu, Yun and Li, Tao and Borji, Ali},
booktitle={Proceedings of the IEEE international conference on computer vision},
pages={4548--4557},
year={2017}
}

% GELU
@article{hendrycks2016gaussian,
title={Gaussian error linear units (gelus)},
author={Hendrycks, Dan and Gimpel, Kevin},
journal={arXiv preprint arXiv:1606.08415},
year={2016}
}

% Instance normalization
@article{ulyanov2016instance,
title={Instance normalization: The missing ingredient for fast stylization},
author={Ulyanov, Dmitry and Vedaldi, Andrea and Lempitsky, Victor},
journal={arXiv preprint arXiv:1607.08022},
year={2016}
}

% Group normalization 
@inproceedings{wu2018group,
title={Group normalization},
author={Wu, Yuxin and He, Kaiming},
booktitle={Proceedings of the European conference on computer vision (ECCV)},
pages={3--19},
year={2018}
}

% timm
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/rwightman/pytorch-image-models}}
}

% taskonomy
@inproceedings{zamir2018taskonomy,
title={Taskonomy: Disentangling task transfer learning},
author={Zamir, Amir R and Sax, Alexander and Shen, William and Guibas, Leonidas J and Malik, Jitendra and Savarese, Silvio},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={3712--3722},
year={2018}
}

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