| What do we understand about convolutional networks? I Hadji, RP Wildes arXiv preprint arXiv:1803.08834, 2018 | 105 | 2018 |
| A Spatiotemporal Oriented Energy Network for Dynamic Texture Recognition I Hadji, RP Wildes IEEE International Conference on Computer Vision (ICCV), 2017 | 27 | 2017 |
| Representation learning via global temporal alignment and cycle-consistency I Hadji, KG Derpanis, AD Jepson Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 24 | 2021 |
| A new large scale dynamic texture dataset with application to convnet understanding I Hadji, RP Wildes Proceedings of the European Conference on Computer Vision (ECCV), 320-335, 2018 | 22 | 2018 |
| Drop-dtw: Aligning common signal between sequences while dropping outliers M Dvornik, I Hadji, KG Derpanis, A Garg, A Jepson Advances in Neural Information Processing Systems 34, 13782-13793, 2021 | 15 | 2021 |
| Local-to-Global Signature Descriptor for 3D Object Recognition I Hadji, GN DeSouza Asian Conference on Computer Vision, 570-584, 2014 | 11 | 2014 |
| What do we understand about convolutional networks? arXiv 2018 I Hadji, RP Wildes arXiv preprint arXiv:1803.08834, 1803 | 5 | 1803 |
| Prediction of diffusional conductance in extracted pore network models using convolutional neural networks N Misaghian, M Agnaou, MA Sadeghi, H Fathiannasab, I Hadji, E Roberts, ... Computers & Geosciences 162, 105086, 2022 | 4 | 2022 |
| P3iv: Probabilistic procedure planning from instructional videos with weak supervision H Zhao, I Hadji, N Dvornik, KG Derpanis, RP Wildes, AD Jepson Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 4 | 2022 |
| Why Convolutional Networks Learn Oriented Bandpass Filters: Theory and Empirical Support I Hadji, RP Wildes arXiv preprint arXiv:2011.14665, 2020 | 2 | 2020 |
| Clustering algorithms used in 3D scene segmentation I Hadji, D Nabelek | 2 | |
| Depth Image Dimension Reduction Using Deep Belief Networks I Hadji, A Jain | 1 | |
| Flow Graph to Video Grounding for Weakly-Supervised Multi-step Localization N Dvornik, I Hadji, H Pham, D Bhatt, B Martinez, A Fazly, AD Jepson Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel …, 2022 | | 2022 |
| Graph2Vid: Flow graph to Video Grounding forWeakly-supervised Multi-Step Localization N Dvornik, I Hadji, H Pham, D Bhatt, B Martinez, A Fazly, AD Jepson arXiv preprint arXiv:2210.04996, 2022 | | 2022 |
| Method and system for learning to temporal align signals with interspersed outliers I Hadji, MA Dvornik, K Derpanis, AD Jepson US Patent App. 17/563,813, 2022 | | 2022 |
| Analytically Defined Spatiotemporal ConvNets for Spacetime Image Understanding I Hadji | | 2019 |
| Bridging the Gap Between Local and Global Approaches for 3D Object Recognition I Hadji University of Missouri-Columbia, 2014 | | 2014 |
| Flow graph to Video Grounding for Weakly-supervised Multi-Step Localization Supplemental Material N Dvornik, I Hadji, H Pham, D Bhatt, B Martinez, A Fazly, AD Jepson | | |
| P3IV: Probabilistic Procedure Planning from Instructional Videos with Weak Supervision Supplemental Material H Zhao, I Hadji, N Dvornik, KG Derpanis, RP Wildes, AD Jepson | | |
Richard WildesProfessor of Electrical Engineering and Computer Science, York UniversityVerified email at cse.yorku.ca
