Trung-Nghia Le

Le, M.-Q., Nguyen, T. V., Le, T.-N., Do, T.-T., Do, M. N., & Tran, M.-T. (2024). MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 38(3), 8. http://doi.org/10.1609/aaai.v38i3.28068

Baraheem, S. S., Le, T.-N., & Nguyen, T. V. (2023). Image synthesis: a review of methods, datasets, evaluation metrics, and future outlook. Artificial Intelligence Review. http://doi.org/10.1007/s10462-023-10434-2

Truong, B., Le, T.-N., Le, K.-D., Tran, M.-T., & Nguyen, T. V. (2022). Public Speaking Simulator with Speech and Audience Feedback. In ISMAR 2022 (p. 4). http://doi.org/10.1109/ISMAR-Adjunct57072.2022.00184

Le, T.-N., Nguyen, T. V., & Tran, M.-T. (2022). Contextual Guided Segmentation Framework for Semi-supervised Video Instance Segmentation. Machine Vision and Applications, 33(2). http://doi.org/10.1007/s00138-022-01278-x

Le, T.-N., Cao, Y., Nguyen, T.-C., Le, M.-Q., Nguyen, K.-D., Do, T.-T., … Nguyen, T. V. (2022). Camouflaged Instance Segmentation In-the-Wild: Dataset, Method, and Benchmark Suite. IEEE Transactions on Image Processing, 31, 14. http://doi.org/10.1109/TIP.2021.3130490

Patel, V. S., Nie, Z., Le, T.-N., & Nguyen, T. V. (2021). Masked Face Analysis via Multi-Task Deep Learning. Journal of Imaging, 7(10), 1. http://doi.org/10.3390/jimaging7100204

Le, T.-N., Nguyen, T. V., Tran, Q.-C., Nguyen, L., Hoang, T.-H., & Tran, M.-T. (2021). Interactive Video Object Mask Annotation. In Proceedings of the AAAI Conference on Artificial Intelligence. Retrieved from https://par.nsf.gov/biblio/10277203

Le, T.-N., Nguyen, V., Le, C., Nguyen, T.-C., & Tran, M.-T. (2021). CamouFinder: Finding Camouflaged Instances in Images. In Proceedings of the AAAI Conference on Artificial Intelligence. Retrieved from https://par.nsf.gov/biblio/10277206

Yan, J., Le, T.-N., Nguyen, K.-D., Tran, M.-T., Do, T.-T., & Nguyen, T. V. (2021). MirrorNet: Bio-Inspired Camouflaged Object Segmentation. IEEE Access, 9, 43301. http://doi.org/10.1109/ACCESS.2021.3064443