Wen-gang Zhou

Computer Vision and Pattern Recognition · 2010–2023

A researcher in the Science corpus: 22 papers; trusts 1,476 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 658 citations into it; #5,019 of 21,575 by trust here, #2,191 by citations
• Artificial Intelligence — 221 citations into it
• Natural Language Processing — 77 citations into it
• Aerospace Engineering — 74 citations into it; #2,175 of 6,421 by trust here, #1,175 by citations
• Model Architectures — 54 citations into it; #2,011 of 2,278 by trust here, #1,548 by citations
• Evaluation & Benchmarks — 46 citations into it
• Safety, Risk, Reliability and Quality — 31 citations into it
• Generative Models — 30 citations into it

Whom Wen-gang Zhou trusts overall

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

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

Who trusts Wen-gang Zhou overall

1. Lingxi Xie
2. Liang Zheng
3. Heliang Zheng
4. Jianwei Yang
5. Jianlong Fu
6. Qi Tian
7. Yiming Li
8. Xiangteng He
9. Yuxin Peng
10. Yi Yang

Papers from Wen-gang Zhou's perspective

Ordered by how strongly the researchers Wen-gang Zhou 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

• The Visual Object Tracking VOT2015 Challenge Results (2015) — 901 citations
• Affinity Derivation and Graph Merge for Instance Segmentation (2018) — 111 citations
• Picking Deep Filter Responses for Fine-Grained Image Recognition (2016) — 339 citations
• Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking (2021) — 739 citations
• The Visual Object Tracking VOT2017 Challenge Results (2017) — 301 citations
• TransVG: End-to-End Visual Grounding with Transformers (2021) — 494 citations
• DIRE for Diffusion-Generated Image Detection (2023) — 554 citations
• Temporal-Channel Transformer for 3D Lidar-Based Video Object Detection in Autonomous Driving (2020) — 170 citations
• The Sixth Visual Object Tracking VOT2018 Challenge Results (2018) — 797 citations
• Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection (2020) — 1,176 citations
• Attribute-assisted reranking for web image retrieval (2012) — 50 citations
• The Eighth Visual Object Tracking VOT2020 Challenge Results (2020) — 345 citations
• Unsupervised Deep Tracking (2019) — 361 citations
• Scalar quantization for large scale image search (2012) — 96 citations
• Soft Contextual Data Augmentation for Neural Machine Translation (2019) — 136 citations
• Video-based Sign Language Recognition without Temporal Segmentation (2018) — 482 citations
• Sign Language Recognition using 3D convolutional neural networks (2015) — 311 citations
• Effective Active Skeleton Representation for Low Latency Human Action Recognition (2016) — 87 citations
• Improving Sign Language Translation with Monolingual Data by Sign Back-Translation (2021) — 352 citations
• Incorporating BERT into Neural Machine Translation (2020)