Artikel dengan mandat akses publik - Zhenguo LiPelajari lebih lanjut
Tersedia di suatu tempat: 38
Detco: Unsupervised contrastive learning for object detection
E Xie, J Ding, W Wang, X Zhan, H Xu, P Sun, Z Li, P Luo
ICCV 2021, 2021
Mandat: Research Grants Council, Hong Kong
Boosting few-shot learning with adaptive margin loss
A Li, W Huang, X Lan, J Feng, Z Li, L Wang
CVPR 2020, 2020
Mandat: National Natural Science Foundation of China
Ddp: Diffusion model for dense visual prediction
Y Ji, Z Chen, E Xie, L Hong, X Liu, Z Liu, T Lu, Z Li, P Luo
ICCV 2023, 2023
Mandat: National Natural Science Foundation of China, Research Grants Council, Hong Kong
An embedding learning framework for numerical features in ctr prediction
H Guo, B Chen, R Tang, W Zhang, Z Li, X He
KDD 2021 oral, 2021
Mandat: National Natural Science Foundation of China
Ordisco: Effective and efficient usage of incremental unlabeled data for semi-supervised continual learning
L Wang, K Yang, C Li, L Hong, Z Li, J Zhu
CVPR 2021, 2021
Mandat: National Natural Science Foundation of China
Cagroup3d: Class-aware grouping for 3d object detection on point clouds
H Wang, L Ding, S Dong, S Shi, A Li, J Li, Z Li, L Wang
NeurIPS 2022, 2022
Mandat: National Natural Science Foundation of China
Diff-instruct: A universal approach for transferring knowledge from pre-trained diffusion models
W Luo, T Hu, S Zhang, J Sun, Z Li, Z Zhang
NeurIPS 2023, 2024
Mandat: National Natural Science Foundation of China
Decaug: Out-of-distribution generalization via decomposed feature representation and semantic augmentation
H Bai, R Sun, L Hong, F Zhou, N Ye, HJ Ye, SHG Chan, Z Li
AAAI 2021, 2021
Mandat: National Natural Science Foundation of China
MOSS-5: A fast method of approximating counts of 5-node graphlets in large graphs
P Wang, J Zhao, X Zhang, Z Li, J Cheng, JCS Lui, D Towsley, J Tao, ...
TKDE 30 (1), 73-86, 2017
Mandat: US Department of Defense, National Natural Science Foundation of China
UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representation
H Wang, H Tang, S Shi, A Li, Z Li, B Schiele, L Wang
ICCV 2023, 2023
Mandat: National Natural Science Foundation of China
Locally differentially private (contextual) bandits learning
K Zheng, T Cai, W Huang, Z Li, L Wang
NeurIPS 2020, 2020
Mandat: National Natural Science Foundation of China
Deepaccident: A motion and accident prediction benchmark for v2x autonomous driving
T Wang, S Kim, J Wenxuan, E Xie, C Ge, J Chen, Z Li, P Luo
AAAI 2024, 2024
Mandat: Research Grants Council, Hong Kong
Nas-ood: Neural architecture search for out-of-distribution generalization
H Bai, F Zhou, L Hong, N Ye, SHG Chan, Z Li
ICCV 2021, 2021
Mandat: Research Grants Council, Hong Kong
New interpretations of normalization methods in deep learning
J Sun, X Cao, H Liang, W Huang, Z Chen, Z Li
AAAI 2020, 2020
Mandat: National Natural Science Foundation of China
Counting triangles in large graphs by random sampling
B Wu, K Yi, Z Li
TKDE 2016 28 (8), 2013-2026, 2016
Mandat: Research Grants Council, Hong Kong
Beyond one-to-one: Rethinking the referring image segmentation
Y Hu, Q Wang, W Shao, E Xie, Z Li, J Han, P Luo
ICCV 2023 oral, 2023
Mandat: National Natural Science Foundation of China, Research Grants Council, Hong Kong
MetaBEV: Solving sensor failures for 3d detection and map segmentation
C Ge, J Chen, E Xie, Z Wang, L Hong, H Lu, Z Li, P Luo
ICCV 2023, 2023
Mandat: National Natural Science Foundation of China, Research Grants Council, Hong Kong
Eyes closed, safety on: Protecting multimodal llms via image-to-text transformation
Y Gou, K Chen, Z Liu, L Hong, H Xu, Z Li, DY Yeung, JT Kwok, Y Zhang
ECCV 2024, 2024
Mandat: Research Grants Council, Hong Kong
DT-Solver: Automated theorem proving with dynamic-tree sampling guided by proof-level value function
H Wang, Y Yuan, Z Liu, J Shen, Y Yin, J Xiong, E Xie, H Shi, Y Li, L Li, ...
ACL 2023, 2023
Mandat: National Natural Science Foundation of China
Rethinking performance estimation in neural architecture search
X Zheng, R Ji, Q Wang, Q Ye, Z Li, Y Tian, Q Tian
CVPR 2020, 2020
Mandat: National Natural Science Foundation of China
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