Ruohan Gao
Computer Vision and Pattern Recognition · 2016–2021
A researcher in the Science corpus: 7 papers; trusts 1,393 researchers and is trusted by 393 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 221 citations into it; #2,089 of 21,575 by trust here, #5,987 by citations
- Signal Processing — 121 citations into it; #1,683 of 7,572 by trust here, #1,592 by citations
- Artificial Intelligence — 97 citations into it; #10,658 of 35,345 by trust here, #16,471 by citations
- Multimodal Learning — 65 citations into it; #530 of 1,211 by trust here, #810 by citations
- Representation & Self-Supervised Learning — 63 citations into it; #1,518 of 3,145 by trust here, #2,488 by citations
- Speech & Audio — 34 citations into it; #636 of 1,076 by trust here, #797 by citations
- Model Architectures — 23 citations into it
- Cognitive Neuroscience — 22 citations into it; #5,039 of 10,852 by trust here, #6,723 by citations
Whom Ruohan Gao trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Andrew Zisserman
- Alex Krizhevsky
- Diederik P. Kingma
- Ross B. Girshick
- Yoshua Bengio
- Yann LeCun
- Karen Simonyan
- Ilya Sutskever
Who trusts Ruohan Gao overall
- Kristen Grauman
- Yuki M. Asano
- Mandela Patrick
- Dinesh Jayaraman
- Ruth C. Fong
- Andrea Vedaldi
- Arda Senocak
- Triantafyllos Afouras
- Andrew Owens
- Rameswar Panda
Papers from Ruohan Gao's perspective
Ordered by how strongly the researchers Ruohan Gao 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
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Learning to Separate Object Sounds by Watching Unlabeled Video (2018) — 298 citations
- 2.5D Visual Sound (2018) — 151 citations
- Object-Centric Representation Learning from Unlabeled Videos (2016) — 36 citations
- Listen to Look: Action Recognition by Previewing Audio (2019) — 295 citations
- ShapeCodes: Self-supervised Feature Learning by Lifting Views to Viewgrids (2017) — 15 citations
- VisualVoice: Audio-Visual Speech Separation with Cross-Modal Consistency (2021) — 261 citations
- Co-Separating Sounds of Visual Objects (2019) — 224 citations