Jin Tang

Computer Vision and Pattern Recognition · IEEE Transactions on Image Processing · 2016–2021

A researcher in the Science corpus: 5 papers; trusts 434 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 136 citations into it; #11,805 of 21,575 by trust here, #10,154 by citations
• Artificial Intelligence — 29 citations into it
• Aerospace Engineering — 27 citations into it
• Graph Learning — 11 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Electrical and Electronic Engineering — 6 citations into it

Whom Jin Tang trusts overall

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

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

Who trusts Jin Tang overall

1. Xingchen Zhang
2. Hui Li

Papers from Jin Tang's perspective

Ordered by how strongly the researchers Jin Tang 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• RGB-T Object Tracking: Benchmark and Baseline (2018) — 585 citations
• LasHeR: A Large-Scale High-Diversity Benchmark for RGBT Tracking (2021) — 319 citations
• SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing (2020) — 354 citations
• Semi-Supervised Learning With Graph Learning-Convolutional Networks (2019) — 452 citations
• Learning Collaborative Sparse Representation for Grayscale-Thermal Tracking (2016) — 356 citations