Youshan Zhang

Youshan Zhang

Assistant Professor at Yeshiva University

Publications

More articles are available at my Google Scholar.

  1. Zhang, Y., & Li, J. (2023). BirdSoundsDenoising: Deep Visual Audio Denoising for Bird Sounds. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. [paper] [Supp] [code]
  2. Li, J,, Guo L., Zhang, Y. (2022). SCORN: Sinter Composition Optimization with Regressive Convolutional Neural Network. Solids. [paper][code]
  3. Zhang, Y., Wieland, M., & Basran, P. S. (2022). Unsupervised Few Shot Key Frame Extraction for Cow Teat Videos. Data, 7(5), 68. [paper][code]
  4. Zhang, Y., Porter, I. R., Wieland, M., & Basran, P. S. (2022). Separable Confident Transductive Learning for Dairy Cows Teat-End Condition Classification. Animals 2022, 12, 886. [paper][code]
  5. Adrien-Maxence H., Zhang Y., Basran, P. S. (2022). Artificial Intelligence 101 for Veterinary Diagnostic Imaging. Veterinary Radiology & Ultrasound
  6. Basran, P. S., DiLeo, C., Zhang, Y., Porter, I. R., & Wieland, M. (2022). Delta thermal radiomics: An application in dairy cow teats. JDS Communications, 3(2), 132-137. [paper]
  7. Zhang, Y., Basran, P. S., Porter, I. R., & Wieland, M. (2022). Dairy Cow Teat-end Condition Classification Using Separable Transductive Learning. 61st National Mastitis Council (NMC) Annual Meeting 2022. [paper]
  8. Zhang, Y. (2022). A survey of unsupervised domain adaptation for visual recognition. [paper]
  9. Zhang, Y., & Davison, B. D. (2021). Deep Least Squares Alignment for Unsupervised Domain Adaptation. British Machine Vision Conference (BMVC) 2021. [paper]
  10. Zhang, Y., & Davison, B. D. (2021). Unsupervised Domain Adaptation for Visual Recognition. ProQuest. [paper]
  11. Zhang, Y., & Davison, B. D. (2021). Weighted Pseudo Labeling Refinement for Plant Identification. CLEF working notes 2021, CLEF: Conference and Labs of the Evaluation Forum. [paper]
  12. Zhang, Y., Davison, B. D., Talghader, V. W., Chen, Z., Xiao, Z., & Kunkel, G. J. (2021, November). Automatic head overcoat thickness measure with NASNet-large-decoder net. In </em>Proceedings of the Future Technologies Conference</em> (pp. 159-176). Springer, Cham. [paper]
  13. Zhang, Y., & Davison, B. D. (2021, September). Enhanced separable disentanglement for unsupervised domain adaptation. In 2021 IEEE International Conference on Image Processing (ICIP) (pp. 784-788). IEEE. [paper]
  14. Zhang, Y., & Davison, B. D. (2021). Deep spherical manifold gaussian kernel for unsupervised domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 4443-4452). [paper]
  15. Zhang, Y., & Davison, B. D. (2021). Efficient pre-trained features and recurrent pseudo-labeling in unsupervised domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 2719-2728). [paper]
  16. Zhang, Y., & Davison, B. D. (2021, June). Correlated adversarial joint discrepancy adaptation network. In 2021 International Conference on Content-Based Multimedia Indexing (CBMI) (pp. 1-6). [paper]
  17. Zhang, Y., & Davison, B. D. (2021, June). Adversarial regression learning for bone age estimation. In International Conference on Information Processing in Medical Imaging (pp. 742-754). Springer, Cham. [paper]
  18. Zhang, Y., & Davison, B. D. (2021, January). Adversarial continuous learning in unsupervised domain adaptation. In International Conference on Pattern Recognition Workshops (pp. 672-687). Springer, Cham. [paper]
  19. Zhang, Y., Ye, H., & Davison, B. D. (2021). Adversarial reinforcement learning for unsupervised domain adaptation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 635-644). [paper]
  20. Zhang, Y., & Davison, B. D. (2020). Adversarial consistent learning on partial domain adaptation of PlantCLEF 2020 challenge. CLEF working notes 2021, CLEF: Conference and Labs of the Evaluation Forum. [paper]
  21. Zhang, Y. (2020). Bayesian geodesic regression on Riemannian manifolds. British Machine Vision Conference (BMVC) 2020. [paper]
  22. Zhang, Y., & Davison, B. D. (2021). Domain adaptation for object recognition using subspace sampling demons. Multimedia Tools and Applications, 80(15), 23255-23274. [paper]
  23. Wu, Y., & Zhang, Y. (2020, September). Mixing deep visual and textual features for image regression. In Proceedings of SAI Intelligent Systems Conference (pp. 747-760). Springer, Cham. [paper]
  24. Zhang, Y., & Davison, B. D. (2020). Impact of imagenet model selection on domain adaptation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (pp. 173-182). [paper]
  25. Zhang, Y. (2019, October). A fast multi-phases demon image registration for atlas building. In Proceedings of the Future Technologies Conference (pp. 112-121). Springer, Cham. [paper]
  26. Zhang, Y. (2019, October). K-means principal geodesic analysis on riemannian manifolds. In Proceedings of the Future Technologies Conference (pp. 578-589). Springer, Cham.[paper]
  27. Ding, H., Tian, Y., Peng, C., Zhang, Y., & Xiang, S. (2020). Inference attacks on genomic privacy with an improved HMM and an RCNN model for unrelated individuals. Information Sciences, 512, 207-218. [paper]
  28. Zhang, Y., & Davison, B. D. (2019, September). Shapenet: Age-focused landmark shape prediction with regressive cnn. In 2019 International Conference on Content-Based Multimedia Indexing (CBMI) (pp. 1-6). IEEE. [paper]
  29. Zhang, Y., Xie, S., & Davison, B. D. (2019, September). Transductive Learning Via Improved Geodesic Sampling. In BMVC (p. 122). [paper]
  30. Zhang, Y., Xing, J., & Zhang, M. (2019). Mixture probabilistic principal geodesic analysis. In Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy (pp. 196-208). Springer, Cham. [paper]
  31. Zhang, Youshan, and Brian D. Davison. “Modified distribution alignment for domain adaptation with pre-trained Inception ResNet.” arXiv preprint arXiv:1904.02322 (2019). [paper]
  32. Zhang, Y. (2019, April). Bayesian estimation for fast sequential diffeomorphic image variability. In Science and Information Conference (pp. 687-699). Springer, Cham. [paper]
  33. Zhang, Y. (2018, October). Corticospinal Tract (CST) Reconstruction Based on Fiber Orientation Distributions (FODs) Tractography. In 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE) (pp. 305-310). IEEE. [paper]
  34. Pan, Z., Zhong, J., Xie, S., Yu, L., Wu, C., Ha, Y. Zhang., & Cao, K. (2019). Accuracy and safety of lateral vertebral notch-referred technique used in subaxial cervical pedicle screw placement. Operative Neurosurgery, 17(1), 52-60. [paper]
  35. Zhang, Y., Allem, J. P., Unger, J. B., & Cruz, T. B. (2018). Automated identification of hookahs (waterpipes) on instagram: an application in feature extraction using convolutional neural network and support vector machine classification. Journal of medical Internet research, 20(11), e10513. [paper]
  36. Zhang, Y., & Li, Q. (2019, March). A regressive convolution neural network and support vector regression model for electricity consumption forecasting. In Future of Information and Communication Conference (pp. 33-45). Springer, Cham. [paper]
  37. Zhang, Y., Guo, L., Li, Q., & Li, J. (2016, December). Electricity consumption forecasting method based on MPSO-BP neural network model. In 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016) (pp. 674-678). Atlantis Press. [paper]
  38. Guo, L. D., Nie, J., & Zhang, Y. S. (2014). Robust exponential stability of stochastic discrete-time BAM neural networks with Markovian jumping parameters and delays. In Advanced Materials Research (Vol. 989, pp. 1877-1882). Trans Tech Publications Ltd.