| Scaling out-of-distribution detection for real-world settings D Hendrycks, S Basart, M Mazeika, A Zou, J Kwon, M Mostajabi, ... arXiv preprint arXiv:1911.11132, 2019 | 733 | 2019 |
| Feedforward semantic segmentation with zoom-out features M Mostajabi, P Yadollahpour, G Shakhnarovich IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 | 591 | 2015 |
| Diode: A dense indoor and outdoor depth dataset I Vasiljevic, N Kolkin, S Zhang, R Luo, H Wang, FZ Dai, AF Daniele, ... arXiv preprint arXiv:1908.00463, 2019 | 338 | 2019 |
| A benchmark for anomaly segmentation D Hendrycks, S Basart, M Mazeika, M Mostajabi, J Steinhardt, D Song arXiv preprint arXiv:1911.11132 2 (7), 2019 | 81 | 2019 |
| High-resolution radar dataset for semi-supervised learning of dynamic objects M Mostajabi, CM Wang, D Ranjan, G Hsyu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 80 | 2020 |
| Regularizing deep networks by modeling and predicting label structure M Mostajabi, M Maire, G Shakhnarovich IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 | 35 | 2018 |
| Diverse sampling for self-supervised learning of semantic segmentation M Mostajabi, N Kolkin, G Shakhnarovich arXiv preprint arXiv:1612.01991, 2016 | 4 | 2016 |
| A robust multilevel segment description for multi-class object recognition M Mostajabi, I Gholampour Machine Vision and Applications 26 (1), 15-30, 2015 | 4 | 2015 |
| Improving and assessing anomaly detectors for large-scale settings D Hendrycks, S Basart, M Mazeika, A Zou, J Kwon, M Mostajabi, ... | 3 | 2022 |
| A framework based on the affine invariant regions for improving unsupervised image segmentation M Mostajabi, I Gholampour 2012 11th International Conference on Information Science, Signal Processing …, 2012 | 2 | 2012 |
| Learning Rich Representations For Structured Visual Prediction Tasks M Mostajabi arXiv preprint arXiv:1908.11820, 2019 | | 2019 |
Greg ShakhnarovichProfessor, TTI-ChicagoVerified email at ttic.edu
