| -SNE: Domain Adaptation using Stochastic Neighborhood Embedding X Xu*, X Zhou*, R Venkatesan, G Swaminathan, O Majumder IEEE Conference on Computer Vision and Pattern Recognition, 2497-2506, 2019 | 157 | 2019 |
| Visual prompt tuning for test-time domain adaptation Y Gao, X Shi, Y Zhu, H Wang, Z Tang, X Zhou, M Li, DN Metaxas arXiv preprint arXiv:2210.04831, 2022 | 76 | 2022 |
| Superpixel-based active learning and online feature importance learning for hyperspectral image analysis J Guo, X Zhou, J Li, A Plaza, S Prasad IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2017 | 43 | 2017 |
| Deep feature alignment neural networks for domain adaptation of hyperspectral data X Zhou, S Prasad IEEE Transactions on Geoscience and Remote Sensing 56 (10), 5863-5872, 2018 | 41 | 2018 |
| Wavelet-domain multiview active learning for spatial-spectral hyperspectral image classification X Zhou, S Prasad, MM Crawford IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2016 | 40 | 2016 |
| Active and semisupervised learning with morphological component analysis for hyperspectral image classification X Zhou, S Prasad IEEE Geoscience and Remote Sensing Letters 14 (8), 1348-1352, 2017 | 28 | 2017 |
| Affordancellm: Grounding affordance from vision language models S Qian, W Chen, M Bai, X Zhou, Z Tu, LE Li Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 24 | 2024 |
| Domain adaptation for robust classification of disparate hyperspectral images X Zhou, S Prasad IEEE Transactions on Computational Imaging 3 (4), 822-836, 2017 | 22 | 2017 |
| Wavelet domain multi-view active learning for hyperspectral image analysis X Zhou, S Prasad, M Crawford 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in …, 2014 | 13 | 2014 |
| Advances in deep learning for hyperspectral image analysis—addressing challenges arising in practical imaging scenarios X Zhou, S Prasad Hyperspectral Image Analysis: Advances in Machine Learning and Signal …, 2020 | 11 | 2020 |
| Vigor: Improving visual grounding of large vision language models with fine-grained reward modeling S Yan, M Bai, W Chen, X Zhou, Q Huang, LE Li arXiv preprint arXiv:2402.06118, 2024 | 9 | 2024 |
| Crowd-sourced artificial intelligence image processing services V Khare, G Swaminathan, Z Xiong US Patent 10,360,482, 2019 | 9 | 2019 |
| Generating artificial intelligence image processing services LP Dirac, V Khare, G Swaminathan, Z Xiong US Patent 10,474,926, 2019 | 5 | 2019 |
| Searching compression profiles for trained neural networks R Venkatesan, G Swaminathan, Z Xiong, R Luo, V Khare US Patent App. 18/334,091, 2023 | 4 | 2023 |
| Exploiting invariance in training deep neural networks C Ye, X Zhou, T McKinney, Y Liu, Q Zhou, F Zhdanov Proceedings of the AAAI Conference on Artificial Intelligence 36 (8), 8849-8856, 2022 | 4 | 2022 |
| Transformation learning based domain adaptation for robust classification of disparate hyperspectral data X Zhou, S Prasad Geoscience and Remote Sensing Symposium (IGARSS), 2017 IEEE International …, 2017 | 4 | 2017 |
| Finetext: text classification via attention-based language model fine-tuning Y Tao, S Gupta, S Krishna, X Zhou, O Majumder, V Khare arXiv preprint arXiv:1910.11959, 2019 | 3 | 2019 |
| Mapping mangrove communities in coastal wetlands using airborne hyperspectral data X Zhou, AR Armitage, S Prasad Hyperspectral Image and Signal Processing: Evolution in Remote Sensing …, 2016 | 3 | 2016 |
| Classification of multi-source sensor data with limited labeled data MM Crawford, S Prasad, X Zhou, Z Zhang Algorithms and Technologies for Multispectral, Hyperspectral, and …, 2015 | 3 | 2015 |
| Out-of-the-box channel pruned networks R Venkatesan, G Swaminathan, X Zhou, A Luo arXiv preprint arXiv:2004.14584, 2020 | 2 | 2020 |
Saurabh PrasadProfessor, University of HoustonVerified email at central.uh.edu
