Jian Liang

Artificial Intelligence · International Journal of Computer Vision · 2018–2023

A researcher in the Science corpus: 11 papers; trusts 1,728 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 514 citations into it; #7,968 of 35,345 by trust here, #10,065 by citations
• Computer Vision and Pattern Recognition — 465 citations into it; #5,773 of 21,575 by trust here, #6,737 by citations
• Representation & Self-Supervised Learning — 249 citations into it; #1,521 of 3,145 by trust here, #1,943 by citations
• Radiology, Nuclear Medicine and Imaging — 82 citations into it; #698 of 3,265 by trust here, #1,462 by citations
• Optimization & Training — 50 citations into it
• Meta & Continual Learning — 38 citations into it
• Cancer Research — 37 citations into it
• Evaluation & Benchmarks — 26 citations into it

Whom Jian Liang trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Diederik P. Kingma
6. Mingsheng Long
7. Ross B. Girshick
8. Yann LeCun
9. Ilya Sutskever
10. Karen Simonyan

Who trusts Jian Liang overall

1. Shuaicheng Niu
2. Yuxi Wang
3. Hongbin Lin
4. Dapeng Hu
5. Yifan Zhang
6. Junyuan Hong
7. Dequan Wang
8. Yusuke Iwasawa
9. Umberto Michieli
10. Yuang Liu

Papers from Jian Liang's perspective

Ordered by how strongly the researchers Jian Liang trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation (2020) — 1,724 citations
• Give Me Your Trained Model: Domain Adaptive Semantic Segmentation without Source Data (2021) — 9 citations
• A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation (2020) — 123 citations
• Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-free Approach (2018) — 326 citations
• A Comprehensive Survey on Test-Time Adaptation Under Distribution Shifts (2023) — 561 citations
• Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning (2021) — 84 citations
• Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning (2021) — 81 citations
• No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data (2021) — 483 citations
• Adversarial Domain Adaptation With Prototype-Based Normalized Output Conditioner (2020) — 38 citations
• Source Data-Absent Unsupervised Domain Adaptation Through Hypothesis Transfer and Labeling Transfer (2020) — 358 citations
• Distant Supervised Centroid Shift: A Simple and Efficient Approach to Visual Domain Adaptation (2019) — 92 citations