Jing Shao

Computer Vision and Pattern Recognition · IEEE International Conference on Computer Vision · 2017–2022

A researcher in the Science corpus: 15 papers; trusts 1,357 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 469 citations into it; #4,652 of 21,575 by trust here, #2,214 by citations
• Artificial Intelligence — 251 citations into it; #18,142 of 35,345 by trust here, #4,923 by citations
• Model Architectures — 88 citations into it
• Multimodal Learning — 66 citations into it
• Generative Models — 59 citations into it; #1,178 of 1,956 by trust here, #1,715 by citations
• Representation & Self-Supervised Learning — 48 citations into it
• Optimization & Training — 37 citations into it
• Biomedical Engineering — 34 citations into it

Whom Jing Shao trusts overall

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

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

Who trusts Jing Shao overall

1. Purushotham Kamath
2. Hanxiao Liu
3. Hui Li
4. Dae Young Park
5. Luke Melas-Kyriazi

Papers from Jing Shao's perspective

Ordered by how strongly the researchers Jing Shao 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

• Practical Block-Wise Neural Network Architecture Generation (2017) — 531 citations
• Spindle Net: Person Re-identification with Human Body Region Guided Feature Decomposition and Fusion (2017) — 870 citations
• Semantics Disentangling for Text-To-Image Generation (2019) — 208 citations
• Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis (2019) — 231 citations
• Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm (2021) — 583 citations
• HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis (2017) — 562 citations
• CAMP: Cross-Modal Adaptive Message Passing for Text-Image Retrieval (2019) — 356 citations
• Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection (2018) — 130 citations
• Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues (2020) — 1,166 citations
• CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations (2020) — 234 citations
• BlockQNN: Efficient Block-Wise Neural Network Architecture Generation (2018) — 137 citations
• ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning for Action Recognition (2022) — 316 citations
• Morphing and Sampling Network for Dense Point Cloud Completion (2019) — 375 citations
• Avatar-Net: Multi-scale Zero-Shot Style Transfer by Feature Decoration (2018) — 325 citations
• Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification (2017) — 359 citations