| Understanding how image quality affects deep neural networks S Dodge, L Karam 2016 eighth international conference on quality of multimedia experience …, 2016 | 1027 | 2016 |
| A study and comparison of human and deep learning recognition performance under visual distortions S Dodge, L Karam 2017 26th international conference on computer communication and networks …, 2017 | 574 | 2017 |
| Finding task-relevant features for few-shot learning by category traversal H Li, D Eigen, S Dodge, M Zeiler, X Wang Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 461 | 2019 |
| MM1: methods, analysis and insights from multimodal LLM pre-training B McKinzie, Z Gan, JP Fauconnier, S Dodge, B Zhang, P Dufter, D Shah, ... European Conference on Computer Vision, 304-323, 2024 | 203 | 2024 |
| Parsing floor plan images S Dodge, J Xu, B Stenger 2017 Fifteenth IAPR international conference on machine vision applications …, 2017 | 162 | 2017 |
| Unconstrained ear recognition using deep neural networks S Dodge, J Mounsef, L Karam IET Biometrics 7 (3), 207-214, 2018 | 91 | 2018 |
| Visual saliency prediction using a mixture of deep neural networks SF Dodge, LJ Karam IEEE Transactions on Image Processing 27 (8), 4080-4090, 2018 | 62 | 2018 |
| Quality resilient deep neural networks S Dodge, L Karam arXiv preprint arXiv:1703.08119, 2017 | 62 | 2017 |
| Quality robust mixtures of deep neural networks SF Dodge, LJ Karam IEEE Transactions on Image Processing 27 (11), 5553-5562, 2018 | 56 | 2018 |
| Human and DNN classification performance on images with quality distortions: A comparative study S Dodge, L Karam ACM Transactions on Applied Perception (TAP) 16 (2), 1-17, 2019 | 44 | 2019 |
| Can the early human visual system compete with Deep Neural Networks? S Dodge, L Karam Proceedings of the IEEE International Conference on Computer Vision …, 2017 | 20 | 2017 |
| Mm1. 5: Methods, analysis & insights from multimodal llm fine-tuning H Zhang, M Gao, Z Gan, P Dufter, N Wenzel, F Huang, D Shah, X Du, ... arXiv preprint arXiv:2409.20566, 2024 | 17 | 2024 |
| Locally adaptive statistical background modeling with deep learning-based false positive rejection for defect detection in semiconductor units BM Haddad, SF Dodge, LJ Karam, NS Patel, MW Braun IEEE Transactions on Semiconductor Manufacturing 33 (3), 357-372, 2020 | 16 | 2020 |
| Systems, techniques, and interfaces for obtaining and annotating training instances M Zeiler, J Rappaport, S Dodge, M Gormish US Patent 11,030,492, 2021 | 13 | 2021 |
| The effect of distortions on the prediction of visual attention MS Gide, SF Dodge, LJ Karam arXiv preprint arXiv:1604.03882, 2016 | 7 | 2016 |
| Haoxuan You, Zirui Wang, Afshin Dehghan, Peter Grafisch, and Yinfei Yang H Zhang, M Gao, Z Gan, P Dufter, N Wenzel, F Huang, D Shah, X Du, ... Mm1, 0 | 6 | |
| Mm1: methods, analysis & insights from multimodal llm pre-training. ArXiv B McKinzie, Z Gan, J Fauconnier, S Dodge, B Zhang, P Dufter, D Shah, ... Preprint posted online on April 18, 2024 | 5 | 2024 |
| Systems, methods, and media for identifying object characteristics based on fixation points L Karam, S Dodge US Patent 9,501,710, 2016 | 4 | 2016 |
| Visual attention quality database for benchmarking performance evaluation metrics MS Gide, SF Dodge, LJ Karam 2016 IEEE International Conference on Image Processing (ICIP), 2792-2796, 2016 | 2 | 2016 |
| Attentive gesture recognition SF Dodge, LJ Karam 2012 19th IEEE International Conference on Image Processing, 177-180, 2012 | 2 | 2012 |
Lina KaramEmerita Professor, Arizona State University; IEEE FellowПотвърден имейл адрес: asu.edu
