Chi Zhang

Computer Vision and Pattern Recognition · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2017–2024

A researcher in the Science corpus: 14 papers; trusts 1,203 researchers and is trusted by 48 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 418 citations into it; #4,056 of 21,575 by trust here, #3,323 by citations
• Artificial Intelligence — 286 citations into it; #4,650 of 35,345 by trust here, #4,353 by citations
• Representation & Self-Supervised Learning — 100 citations into it
• Meta & Continual Learning — 72 citations into it; #333 of 1,041 by trust here, #169 by citations
• Evaluation & Benchmarks — 43 citations into it
• Media Technology — 41 citations into it
• Model Architectures — 41 citations into it
• Aerospace Engineering — 39 citations into it

Whom Chi Zhang trusts overall

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

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

Who trusts Chi Zhang overall

1. Guosheng Lin
2. Da-Wei Zhou
3. Han-Jia Ye
4. Yaoyao Liu
5. Xiaoyu Chen
6. Guangchen Shi
7. Binghao Liu
8. Weide Liu
9. Guankai Li
10. Zhuotao Tian

Papers from Chi Zhang's perspective

Ordered by how strongly the researchers Chi Zhang 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• CANet: Class-Agnostic Segmentation Networks With Iterative Refinement and Attentive Few-Shot Learning (2019) — 674 citations
• Pyramid Graph Networks With Connection Attentions for Region-Based One-Shot Semantic Segmentation (2019) — 358 citations
• Few-Shot Incremental Learning with Continually Evolved Classifiers (2021) — 428 citations
• DeepEMD: Few-Shot Image Classification With Differentiable Earth Mover’s Distance and Structured Classifiers (2020) — 903 citations
• Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image (2023) — 446 citations
• CRNet: Cross-Reference Networks for Few-Shot Segmentation (2020) — 240 citations
• AppAgent: Multimodal Agents as Smartphone Users (2023) — 479 citations
• Conditional Gaussian Distribution Learning for Open Set Recognition (2020) — 274 citations
• Metric3D v2: A Versatile Monocular Geometric Foundation Model for Zero-Shot Metric Depth and Surface Normal Estimation (2024) — 489 citations
• CycleSegNet: Object Co-Segmentation With Cycle Refinement and Region Correspondence (2021) — 28 citations
• DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning (2020) — 186 citations
• Weakly Supervised Segmentation with Maximum Bipartite Graph Matching (2020) — 42 citations
• Compositional Prototype Network with Multi-view Comparision for Few-Shot Point Cloud Semantic Segmentation (2020) — 16 citations
• Efficient eye typing with 9-direction gaze estimation (2017) — 48 citations