Yunchao Gong
Computer Vision and Pattern Recognition · 2011–2014
A researcher in the Science corpus: 8 papers; trusts 981 researchers and is trusted by 3,728 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 210 citations into it; #545 of 21,575 by trust here, #1,008 by citations
- Artificial Intelligence — 85 citations into it; #2,313 of 35,345 by trust here, #2,522 by citations
- Representation & Self-Supervised Learning — 48 citations into it; #281 of 3,145 by trust here, #704 by citations
- Media Technology — 28 citations into it; #106 of 3,621 by trust here, #251 by citations
- Aerospace Engineering — 26 citations into it
- Information Retrieval & Search — 25 citations into it
- Multimodal Learning — 22 citations into it; #255 of 1,211 by trust here, #377 by citations
- Evaluation & Benchmarks — 19 citations into it
Whom Yunchao Gong trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- David G. Lowe
- Antonio Torralba
- Yann LeCun
- Yoshua Bengio
- Ilya Sutskever
- Rob Fergus
- Kaiming He
- Andrew Zisserman
Who trusts Yunchao Gong overall
- Jingdong Wang
- Wei David Liu
- Kaiming He
- Fumin Shen
- Mingsheng Long
- Chunhua Shen
- Yi Yang
- Dacheng Tao
- Albert Gordo
- Mohammad Norouzi
Papers from Yunchao Gong's perspective
Ordered by how strongly the researchers Yunchao Gong 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)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Improving Image-Sentence Embeddings Using Large Weakly Annotated Photo Collections (2014) — 315 citations
- Multi-scale Orderless Pooling of Deep Convolutional Activation Features (2014) — 1,117 citations
- A Multi-View Embedding Space for Modeling Internet Images, Tags, and Their Semantics (2012) — 588 citations
- Iterative quantization: A procrustean approach to learning binary codes (2011) — 1,206 citations
- Compressing Deep Convolutional Networks using Vector Quantization (2014) — 1,233 citations
- Deep Convolutional Ranking for Multilabel Image Annotation (2013) — 458 citations
- Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval (2013) — 1,709 citations
- Circulant Binary Embedding (2014) — 185 citations