Bingyi Kang

Computer Vision and Pattern Recognition · 2018–2025

A researcher in the Science corpus: 16 papers; trusts 1,268 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 526 citations into it; #6,443 of 21,575 by trust here, #3,166 by citations
• Artificial Intelligence — 431 citations into it; #31,962 of 35,345 by trust here, #5,059 by citations
• Evaluation & Benchmarks — 96 citations into it
• Representation & Self-Supervised Learning — 96 citations into it
• Model Architectures — 82 citations into it
• Optimization & Training — 64 citations into it
• Reinforcement Learning — 59 citations into it
• Radiology, Nuclear Medicine and Imaging — 54 citations into it; #3,194 of 3,265 by trust here, #622 by citations

Whom Bingyi Kang trusts overall

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

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

Who trusts Bingyi Kang overall

1. Yu Li
2. Scott Fujimoto
3. Xiuye Gu
4. Shipeng Yan
5. Junnan Li
6. Songyang Zhang
7. Tsung-Yi Lin
8. Jiashi Feng
9. Yifan Zhang
10. Zhisheng Zhong

Papers from Bingyi Kang's perspective

Ordered by how strongly the researchers Bingyi Kang 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. Going deeper with convolutions

Their papers

• DeepViT: Towards Deeper Vision Transformer (2021) — 638 citations
• Decoupling Representation and Classifier for Long-Tailed Recognition (2019) — 1,567 citations
• Few-Shot Object Detection via Feature Reweighting (2018) — 862 citations
• Overcoming Classifier Imbalance for Long-Tail Object Detection With Balanced Group Softmax (2020) — 306 citations
• The Devil is in Classification: A Simple Framework for Long-tail Instance Segmentation (2020) — 207 citations
• Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data (2024) — 1,978 citations
• The Devil is in Classification: A Simple Framework for Long-tail Object Detection and Instance Segmentation (2020) — 7 citations
• Refiner: Refining Self-attention for Vision Transformers (2021) — 69 citations
• Regularization Matters in Policy Optimization (2019) — 39 citations
• Bag of Tricks for Training Data Extraction from Language Models (2023) — 95 citations
• Deep Long-Tailed Learning: A Survey (2021) — 927 citations
• Depth Anything 3: Recovering the Visual Space from Any Views (2025) — 446 citations
• Depth Anything V2 (2024) — 1,926 citations
• Exploring Balanced Feature Spaces for Representation Learning (2021) — 303 citations
• Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective (2020)
• Policy Optimization with Demonstrations (2018) — 174 citations