Jue Wang

Computer Vision and Pattern Recognition · with Cherian · 2017–2022

A researcher in the Science corpus: 8 papers; trusts 1,240 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 369 citations into it; #15,760 of 21,575 by trust here, #6,146 by citations
• Artificial Intelligence — 285 citations into it; #25,902 of 35,345 by trust here, #12,244 by citations
• Representation & Self-Supervised Learning — 65 citations into it
• Model Architectures — 63 citations into it
• Evaluation & Benchmarks — 51 citations into it
• Natural Language Processing — 41 citations into it
• Large Language Models — 24 citations into it
• Optimization & Training — 24 citations into it

Whom Jue Wang trusts overall

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

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

Who trusts Jue Wang overall

1. Anoop Cherian
2. Yi Wang
3. Stephen Gould
4. Basura Fernando
5. Kunchang Li

Papers from Jue Wang's perspective

Ordered by how strongly the researchers Jue Wang trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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

• VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training (2022) — 2,096 citations
• VX2TEXT: End-to-End Learning of Video-Based Text Generation From Multimodal Inputs (2021) — 78 citations
• Audio Visual Scene-Aware Dialog (2019) — 230 citations
• End-to-end Audio Visual Scene-aware Dialog Using Multimodal Attention-based Video Features (2018) — 132 citations
• Ordered Pooling of Optical Flow Sequences for Action Recognition (2017) — 47 citations
• Long-Short Temporal Contrastive Learning of Video Transformers (2021) — 62 citations
• AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition (2022) — 1,174 citations
• Video Representation Learning Using Discriminative Pooling (2018) — 63 citations