Yu, Zhiding

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J. M., & Luo, P. (2021) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 5,090

Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-training

Zou, Y., Yu, Z., Vijaya Kumar, B. V. K., & Wang, J. (2018) · Lecture Notes in Computer Science

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 1,464

FocalFormer3D : Focusing on Hard Instance for 3D Object Detection

Chen, Y., Yu, Z., Chen, Y., Lan, S., Anandkumar, A., Jia, J., & Alvarez, J. M. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsAutonomous Vehicle Technology and Safety

Cited by 119

Taxonomy of Machine Learning Safety: A Survey and Primer

Mohseni, S., Wang, H., Xiao, C., Yu, Z., Wang, Z., & Yadawa, J. (2023) · ACM Computing Surveys

Safety Systems Engineering in AutonomyAdversarial Robustness in Machine Learning

Cited by 53

Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection

Elezi, I., Yu, Z., Anandkumar, A., Leal-Taixe, L., & Alvarez, J. M. (2021) · arXiv

Machine Learning and AlgorithmsAdvanced Neural Network Applications

Cited by 50

GR00T N1: An Open Foundation Model for Generalist Humanoid Robots

NVIDIA, :, Bjorck, J., Castañeda, F., Cherniadev, N., Da, X., Ding, R., Fan, L. "., Fang, Y., Fox, D., et al. (2025) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 10

STORM: Token-Efficient Long Video Understanding for Multimodal LLMs

Jiang, J., Li, X., Liu, Z., Li, M., Chen, G., Li, Z., Huang, D.-A., Liu, G., Yu, Z., Keutzer, K., et al. (2025) · 2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

Multimodal Machine Learning ApplicationsHuman Pose and Action Recognition

Cited by 2

Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models

Li, Z., Chen, G., Liu, S., Wang, S., VS, V., Ji, Y., Lan, S., Zhang, H., Zhao, Y., Radhakrishnan, S., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 2

Argus: Vision-Centric Reasoning with Grounded Chain-of-Thought

Man, Y., Huang, D.-A., Liu, G., Sheng, S., Liu, S., Gui, L.-Y., Kautz, J., Wang, Y.-X., & Yu, Z. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsVisual Attention and Saliency Detection

Cited by 2

Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models

NVIDIA, :, Blakeman, A., Basant, A., Khattar, A., Renduchintala, A., Bercovich, A., Ficek, A., Bjorlin, A., Taghibakhshi, A., et al. (2025) · arXiv

Advanced Neural Network ApplicationsTopic Modeling

Cited by 0

Towards Multimodal Lifelong Understanding: A Dataset and Agentic Baseline

Chen, G., Lu, L., Liu, Y., Dong, L., Zou, L., Lv, J., Li, Z., Mao, X., Pei, B., Wang, S., et al. (2026) · arXiv

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 0