Jialin Wu

Computer Vision and Pattern Recognition · 2018–2023

A researcher in the Science corpus: 5 papers; trusts 779 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 203 citations into it; #8,335 of 21,575 by trust here, #7,249 by citations
• Artificial Intelligence — 177 citations into it; #12,487 of 35,345 by trust here, #8,439 by citations
• Multimodal Learning — 62 citations into it; #444 of 1,211 by trust here, #667 by citations
• Natural Language Processing — 47 citations into it
• Control and Systems Engineering — 28 citations into it
• Evaluation & Benchmarks — 27 citations into it
• Question Answering & Reading Comprehension — 21 citations into it; #268 of 335 by trust here, #254 by citations
• Model Architectures — 18 citations into it

Whom Jialin Wu trusts overall

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

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

Who trusts Jialin Wu overall

1. Bodhisattwa Prasad Majumder
2. Oana-Maria Camburu
3. Alon Jacovi
4. Weizhe Lin
5. Yaru Hao
6. Maxime Kayser
7. Thomas Lukasiewicz
8. Ana Marasovic
9. Mengyao Zhai
10. Junjun He

Papers from Jialin Wu's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• PaLI-X: On Scaling up a Multilingual Vision and Language Model (2023) — 301 citations
• Multi-Modal Answer Validation for Knowledge-Based VQA (2021) — 186 citations
• Dynamic Filtering with Large Sampling Field for ConvNets (2018) — 43 citations
• Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0 (2023) — 1,105 citations
• Faithful Multimodal Explanation for Visual Question Answering (2018) — 105 citations