Chong Zhou
Computer Vision and Pattern Recognition · with Bolya · 2019–2021
A researcher in the Science corpus: 4 papers; trusts 1,145 researchers and is trusted by 33 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 181 citations into it; #5,298 of 21,575 by trust here, #5,464 by citations
- Artificial Intelligence — 100 citations into it; #9,410 of 35,345 by trust here, #8,439 by citations
- Representation & Self-Supervised Learning — 46 citations into it; #2,141 of 3,145 by trust here, #2,624 by citations
- Evaluation & Benchmarks — 23 citations into it
- Multimodal Learning — 21 citations into it
- Model Architectures — 12 citations into it
- Media Technology — 7 citations into it
- Meta & Continual Learning — 6 citations into it
Whom Chong Zhou trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Ross B. Girshick
- Geoffrey E. Hinton
- Alex Krizhevsky
- Jia Deng
- Yoshua Bengio
- Shaoqing Ren
- Diederik P. Kingma
- Yann LeCun
- Karen Simonyan
Who trusts Chong Zhou overall
- Daniel Bolya
- Xueyan Zou
- Jianyi Wang
- Siyuan Li
- Haotian Liu
- S. Gadre
- Golnaz Ghiasi
- Jingyi Zhang
- Ziqi Zhou
- Weicheng Kuo
Papers from Chong Zhou's perspective
Ordered by how strongly the researchers Chong Zhou trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- YOLACT: Real-Time Instance Segmentation (2019) — 2,140 citations
- DenseCLIP: Extract Free Dense Labels from CLIP (2021) — 82 citations
- YOLACT++ Better Real-Time Instance Segmentation (2019) — 506 citations
- Extract Free Dense Labels from CLIP (2021) — 806 citations