Weilin Huang

Computer Vision and Pattern Recognition · IEEE International Conference on Computer Vision · 2014–2021

A researcher in the Science corpus: 15 papers; trusts 1,594 researchers and is trusted by 71 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 435 citations into it; #3,430 of 21,575 by trust here, #2,506 by citations
• Artificial Intelligence — 147 citations into it; #10,880 of 35,345 by trust here, #6,330 by citations
• Model Architectures — 69 citations into it
• Media Technology — 56 citations into it; #619 of 3,621 by trust here, #789 by citations
• Evaluation & Benchmarks — 43 citations into it
• Representation & Self-Supervised Learning — 31 citations into it
• Natural Language Processing — 24 citations into it
• Optimization & Training — 22 citations into it

Whom Weilin Huang trusts overall

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

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

Who trusts Weilin Huang overall

1. Minghui Liao
2. Cong Yao
3. Shangbang Long
4. Tong He
5. Xiang Bai
6. Yuliang Liu
7. Baoguang Shi
8. Zheng Zhang
9. Zhengxia Zou
10. Pan He

Papers from Weilin Huang's perspective

Ordered by how strongly the researchers Weilin Huang 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images (2018) — 383 citations
• An End-to-End TextSpotter with Explicit Alignment and Attention (2018) — 226 citations
• Knowledge Guided Disambiguation for Large-Scale Scene Classification With Multi-Resolution CNNs (2016) — 158 citations
• Detecting Text in Natural Image with Connectionist Text Proposal Network (2016) — 1,005 citations
• Multi-Similarity Loss With General Pair Weighting for Deep Metric Learning (2019) — 909 citations
• Deep Metric Learning with Hierarchical Triplet Loss (2018) — 465 citations
• Robust Scene Text Detection with Convolution Neural Network Induced MSER Trees (2014) — 395 citations
• Single Shot Text Detector with Regional Attention (2017) — 315 citations
• Deformable Siamese Attention Networks for Visual Object Tracking (2020) — 369 citations
• Text-Attentional Convolutional Neural Network for Scene Text Detection (2015) — 321 citations
• Reading Scene Text in Deep Convolutional Sequences (2015) — 321 citations
• Decoupling Category-wise Independence and Relevance with Self-attention for Multi-label Image Classification (2019) — 13 citations
• TOOD: Task-aligned One-stage Object Detection (2021) — 1,333 citations
• Knowledge Integration Networks for Action Recognition (2020) — 22 citations
• ClothFlow: A Flow-Based Model for Clothed Person Generation (2019) — 307 citations