Xiaokang Chen

Computer Vision and Pattern Recognition · 2020–2024

A researcher in the Science corpus: 7 papers; trusts 694 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 312 citations into it; #15,172 of 21,575 by trust here, #4,834 by citations
• Artificial Intelligence — 191 citations into it; #27,237 of 35,345 by trust here, #8,525 by citations
• Model Architectures — 54 citations into it; #1,773 of 2,278 by trust here, #1,509 by citations
• Computer Graphics and Computer-Aided Design — 31 citations into it
• Optimization & Training — 25 citations into it
• Biophysics — 7 citations into it
• Radiology, Nuclear Medicine and Imaging — 7 citations into it

Whom Xiaokang Chen trusts overall

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

1. Kaiming He
2. Ross B. Girshick
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yoshua Bengio
6. Diederik P. Kingma
7. Yann LeCun
8. Christian Szegedy
9. Sergey Ioffe
10. Ilya Sutskever

Who trusts Xiaokang Chen overall

1. Lihe Yang
2. Chong Zhou
3. Feng Li
4. Hao Zhang
5. Yanbei Chen
6. Yuyang Zhao
7. Shilong Liu
8. Sheng Fang
9. Xiangde Luo
10. Massih-Reza Amini

Papers from Xiaokang Chen's perspective

Ordered by how strongly the researchers Xiaokang Chen 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
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. Going deeper with convolutions

Their papers

• VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks (2023) — 699 citations
• Conditional DETR for Fast Training Convergence (2021) — 1,038 citations
• Context Autoencoder for Self-supervised Representation Learning (2022) — 508 citations
• Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision (2021) — 1,179 citations
• Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment (2022) — 254 citations
• LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation (2024) — 817 citations
• Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation (2020) — 449 citations