Wei Zhang

Computer Vision and Pattern Recognition · with Li · 2017–2023

A researcher in the Science corpus: 21 papers; trusts 1,629 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 647 citations into it; #10,216 of 21,575 by trust here, #3,827 by citations
• Artificial Intelligence — 504 citations into it; #11,201 of 35,345 by trust here, #4,956 by citations
• Model Architectures — 123 citations into it
• Efficiency & Compression — 115 citations into it; #1,206 of 1,264 by trust here, #620 by citations
• Representation & Self-Supervised Learning — 88 citations into it
• Evaluation & Benchmarks — 76 citations into it
• Natural Language Processing — 73 citations into it
• Optimization & Training — 53 citations into it

Whom Wei Zhang trusts overall

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

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

Who trusts Wei Zhang overall

1. Sehoon Kim
2. Zi-Mei Yao
3. Ruosong Wang
4. Chao Yu
5. Sergey Levine
6. Xiaoxia Wu
7. Shuai Zhang
8. Renrui Zhang
9. A. Gholami
10. Katikapalli Subramanyam Kalyan

Papers from Wei Zhang's perspective

Ordered by how strongly the researchers Wei Zhang 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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

• Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling (2021) — 591 citations
• Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification (2019) — 199 citations
• Co-attending Free-form Regions and Detections with Multi-modal Multiplicative Feature Embedding for Visual Question Answering (2017) — 82 citations
• IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning (2021) — 307 citations
• Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation (2018) — 295 citations
• One Million Scenes for Autonomous Driving: ONCE Dataset (2021) — 383 citations
• Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets (2020) — 111 citations
• SP-NAS: Serial-to-Parallel Backbone Search for Object Detection (2020) — 73 citations
• BinaryBERT: Pushing the Limit of BERT Quantization (2020) — 276 citations
• TernaryBERT: Distillation-aware Ultra-low Bit BERT (2020) — 238 citations
• PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN With Dual-Discriminators (2023) — 302 citations
• Compression of Generative Pre-trained Language Models via Quantization (2022) — 119 citations
• CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks (2021) — 391 citations
• SODA10M: Towards Large-Scale Object Detection Benchmark for Autonomous Driving (2021) — 49 citations
• PointCLIP: Point Cloud Understanding by CLIP (2021) — 671 citations
• Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts (2020) — 36 citations
• VEGA: Towards an End-to-End Configurable AutoML Pipeline (2020) — 12 citations
• CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending (2020) — 246 citations
• Furnishing Your Room by What You See: An End-to-End Furniture Set Retrieval Framework with Rich Annotated Benchmark Dataset (2019) — 10 citations
• SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detection (2019) — 86 citations