Xiansheng Hua

Computer Vision and Pattern Recognition · 2005–2022

A researcher in the Science corpus: 20 papers; trusts 1,620 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 490 citations into it; #8,868 of 21,575 by trust here, #3,267 by citations
• Artificial Intelligence — 351 citations into it; #19,664 of 35,345 by trust here, #6,468 by citations
• Representation & Self-Supervised Learning — 87 citations into it
• Model Architectures — 54 citations into it
• Aerospace Engineering — 53 citations into it
• Multimodal Learning — 44 citations into it
• Evaluation & Benchmarks — 40 citations into it
• Meta & Continual Learning — 38 citations into it; #780 of 1,041 by trust here, #529 by citations

Whom Xiansheng Hua trusts overall

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

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

Who trusts Xiansheng Hua overall

1. Min-Ling Zhang
2. Jun Yu
3. Arthur G. Money
4. Xinting Hu
5. Ofer Dor
6. Yaoyao Liu
7. Tam V. Nguyen
8. Yanwei Fu
9. Kaihua Tang
10. Chi Zhang

Papers from Xiansheng Hua's perspective

Ordered by how strongly the researchers Xiansheng Hua 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. 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

• Distilling Causal Effect of Data in Class-Incremental Learning (2021) — 213 citations
• Structure Aware Single-Stage 3D Object Detection From Point Cloud (2020) — 613 citations
• Unified Video Annotation via Multigraph Learning (2009) — 458 citations
• Correlative multi-label video annotation (2007) — 504 citations
• Improving 3D Object Detection with Channel-wise Transformer (2021) — 306 citations
• A generic framework of user attention model and its application in video summarization (2005) — 551 citations
• Quantization Networks (2019) — 426 citations
• Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach (2020) — 273 citations
• Disentangled Representation Learning for Text-Video Retrieval (2022) — 110 citations
• Half-Real Half-Fake Distillation for Class-Incremental Semantic Segmentation (2021) — 13 citations
• Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression (2021) — 333 citations
• Counterfactual VQA: A Cause-Effect Look at Language Bias (2020) — 539 citations
• Feature Pyramid Transformer (2020) — 306 citations
• Interventional Few-Shot Learning (2020) — 277 citations
• Causal Intervention for Weakly-Supervised Semantic Segmentation (2020) — 548 citations
• HoMM: Higher-order Moment Matching for Unsupervised Domain Adaptation (2019) — 262 citations
• Dynamic Anchor Feature Selection for Single-Shot Object Detection (2019) — 53 citations
• Spatio-Temporal AutoEncoder for Video Anomaly Detection (2017) — 552 citations
• Learning Visual Semantic Relationships for Efficient Visual Retrieval (2015) — 103 citations
• Predicting occupation via human clothing and contexts (2011) — 102 citations