Zhu, Xizhou

Deformable DETR: Deformable Transformers for End-to-End Object Detection

Zhu, X., Su, W., Lu, L., Li, B., Wang, X., & Dai, J. (2020) · arXiv

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 6,027

Deformable ConvNets V2: More Deformable, Better Results

Zhu, X., Hu, H., Lin, S., & Dai, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 2,943

InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions

Wang, W., Dai, J., Chen, Z., Huang, Z., Li, Z., Zhu, X., Hu, X., Lu, T., Lu, L., Li, H., et al. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 1,080

VL-BERT: Pre-training of Generic Visual-Linguistic Representations

Su, W., Zhu, X., Cao, Y., Li, B., Lu, L., Wei, F., & Dai, J. (2019) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 950

Intern VL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks

Chen, Z., Wu, J., Wang, W., Su, W., Chen, G., Xing, S., Zhong, M., Zhang, Q., Zhu, X., Lu, L., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 734

Flow-Guided Feature Aggregation for Video Object Detection

Zhu, X., Wang, Y., Dai, J., Yuan, L., & Wei, Y. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsVideo Surveillance and Tracking Methods

Cited by 729

An Empirical Study of Spatial Attention Mechanisms in Deep Networks

Zhu, X., Cheng, D., Zhang, Z., Lin, S., & Dai, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 659

Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory

Zhu, X., Chen, Y., Tian, H., Tao, C., Su, W., Yang, C., Huang, G., Li, B., Lu, L., Wang, X., et al. (2023) · arXiv

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 33