Jinhui Tang

Computer Vision and Pattern Recognition · 2007–2022

A researcher in the Science corpus: 25 papers; trusts 1,999 researchers and is trusted by 86 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 691 citations into it; #3,468 of 21,575 by trust here, #1,712 by citations
• Artificial Intelligence — 361 citations into it; #20,864 of 35,345 by trust here, #5,720 by citations
• Representation & Self-Supervised Learning — 99 citations into it
• Multimodal Learning — 72 citations into it
• Model Architectures — 57 citations into it
• Evaluation & Benchmarks — 55 citations into it; #1,329 of 3,147 by trust here, #872 by citations
• Computational Mechanics — 46 citations into it; #5,773 of 10,001 by trust here, #6,877 by citations
• Aerospace Engineering — 43 citations into it

Whom Jinhui Tang trusts overall

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

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

Who trusts Jinhui Tang overall

1. Yang Liu
2. Xirong Li
3. Peng Cui
4. Raymond A. Yeh
5. Shiv Ram Dubey
6. Yi Yang
7. Wu-Jun Li
8. Yan Kang
9. Yunchao Gong
10. Zechao Li

Papers from Jinhui Tang's perspective

Ordered by how strongly the researchers Jinhui Tang 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

• NUS-WIDE: a real-world web image database from National University of Singapore (2009) — 3,342 citations
• Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection (2020) — 1,924 citations
• Unified Video Annotation via Multigraph Learning (2009) — 458 citations
• Correlative multi-label video annotation (2007) — 504 citations
• Sparse composite quantization (2015) — 80 citations
• Single Image Dehazing via Conditional Generative Adversarial Network (2018) — 405 citations
• Generalized Nonconvex Nonsmooth Low-Rank Minimization (2014) — 292 citations
• Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection (2020) — 334 citations
• Multimodal Attention with Image Text Spatial Relationship for OCR-Based Image Captioning (2020) — 57 citations
• Few-Shot Image Recognition With Knowledge Transfer (2019) — 232 citations
• Weakly-Shared Deep Transfer Networks for Heterogeneous-Domain Knowledge Propagation (2015) — 212 citations
• Adaptive Context Network for Scene Parsing (2019) — 149 citations
• Body Surface Context: A New Robust Feature for Action Recognition From Depth Videos (2014) — 57 citations
• Weakly-Supervised Dual Clustering for Image Semantic Segmentation (2013) — 76 citations
• Video-Text Pre-training with Learned Regions (2021) — 30 citations
• Describing Trajectory of Surface Patch for Human Action Recognition on RGB and Depth Videos (2015) — 30 citations
• Learning attention-guided pyramidal features for few-shot fine-grained recognition (2022) — 335 citations
• Fine-Grained Image Analysis With Deep Learning: A Survey (2021) — 462 citations
• Feature Pyramid Transformer (2020) — 306 citations
• Causal Intervention for Weakly-Supervised Semantic Segmentation (2020) — 548 citations