Huijuan Xu

Computer Vision and Pattern Recognition · 2014–2020

A researcher in the Science corpus: 7 papers; trusts 1,131 researchers and is trusted by 1,756 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 194 citations into it; #958 of 21,575 by trust here, #1,669 by citations
• Artificial Intelligence — 144 citations into it; #1,867 of 35,345 by trust here, #3,978 by citations
• Multimodal Learning — 56 citations into it; #77 of 1,211 by trust here, #339 by citations
• Natural Language Processing — 45 citations into it; #2,297 of 5,688 by trust here, #1,286 by citations
• Model Architectures — 34 citations into it
• Representation & Self-Supervised Learning — 33 citations into it; #2,482 of 3,145 by trust here, #2,299 by citations
• Evaluation & Benchmarks — 19 citations into it
• Meta & Continual Learning — 12 citations into it

Whom Huijuan Xu trusts overall

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

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

Who trusts Huijuan Xu overall

1. Kushal Kafle
2. Jonghwan Mun
3. Qi Wu
4. Mateusz Malinowski
5. Marcus Rohrbach
6. Trevor Darrell
7. Paul Hongsuck Seo
8. Damien Teney
9. Dong Huk Park
10. Mohit Bansal

Papers from Huijuan Xu's perspective

Ordered by how strongly the researchers Huijuan Xu 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
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

• Translating Videos to Natural Language Using Deep Recurrent Neural Networks (2014) — 989 citations
• Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering (2015) — 787 citations
• R-C3D: Region Convolutional 3D Network for Temporal Activity Detection (2017) — 772 citations
• Multilevel Language and Vision Integration for Text-to-Clip Retrieval (2018) — 369 citations
• A Multi-scale Multiple Instance Video Description Network (2015) — 64 citations
• Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning (2020) — 467 citations
• A New Meta-Baseline for Few-Shot Learning (2020) — 224 citations