Zhaofan Qiu
Computer Vision and Pattern Recognition · 2016–2019
A researcher in the Science corpus: 7 papers; trusts 1,162 researchers and is trusted by 613 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 226 citations into it; #1,731 of 21,575 by trust here, #2,605 by citations
- Artificial Intelligence — 126 citations into it; #12,187 of 35,345 by trust here, #6,796 by citations
- Model Architectures — 42 citations into it
- Evaluation & Benchmarks — 26 citations into it
- Multimodal Learning — 17 citations into it; #442 of 1,211 by trust here, #671 by citations
- Natural Language Processing — 17 citations into it
- Representation & Self-Supervised Learning — 14 citations into it
- Media Technology — 8 citations into it
Whom Zhaofan Qiu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Karen Simonyan
- Alex Krizhevsky
- Geoffrey E. Hinton
- Andrew Zisserman
- Ivan Laptev
- Ross B. Girshick
- Yoshua Bengio
- Ilya Sutskever
- Yann LeCun
Who trusts Zhaofan Qiu overall
- Christoph Feichtenhofer
- Longlong Jing
- Limin Wang
- Ji Lin
- Du Tran
- Ting Yao
- Thao Minh Le
- Kunchang Li
- X. Wang
- Dongliang He
Papers from Zhaofan Qiu's perspective
Ordered by how strongly the researchers Zhaofan Qiu trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks (2017) — 1,834 citations
- Boosting Image Captioning with Attributes (2016) — 656 citations
- Fully Convolutional Adaptation Networks for Semantic Segmentation (2018) — 369 citations
- Customizable Architecture Search for Semantic Segmentation (2019) — 151 citations
- Action Recognition by Learning Deep Multi-Granular Spatio-Temporal Video Representation (2016) — 129 citations
- Learning Spatio-Temporal Representation With Local and Global Diffusion (2019) — 185 citations
- Gaussian Temporal Awareness Networks for Action Localization (2019) — 361 citations