Hao Zhang

Computer Vision and Pattern Recognition · with Li · 2022–2024

A researcher in the Science corpus: 9 papers; trusts 951 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 380 citations into it; #7,058 of 21,575 by trust here, #3,191 by citations
• Artificial Intelligence — 259 citations into it; #15,930 of 35,345 by trust here, #5,317 by citations
• Multimodal Learning — 107 citations into it
• Model Architectures — 87 citations into it; #1,548 of 2,278 by trust here, #794 by citations
• Evaluation & Benchmarks — 41 citations into it
• Natural Language Processing — 37 citations into it
• Optimization & Training — 16 citations into it
• Radiology, Nuclear Medicine and Imaging — 10 citations into it

Whom Hao Zhang trusts overall

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

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

Who trusts Hao Zhang overall

1. Feng Li
2. Shilong Liu
3. Zhuofan Zong
4. Jianwei Yang
5. Lei Ke
6. Siyi Liu
7. Mingqiao Ye
8. Qiang Chen
9. Xiaokang Chen
10. Shaoshuai Shi

Papers from Hao Zhang's perspective

Ordered by how strongly the researchers Hao Zhang 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection (2023) — 4,547 citations
• Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation (2022) — 638 citations
• DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection (2022) — 2,919 citations
• DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR (2022) — 1,335 citations
• DN-DETR: Accelerate DETR Training by Introducing Query DeNoising (2022) — 1,199 citations
• Segment Everything Everywhere All at Once (2023) — 838 citations
• A Simple Framework for Open-Vocabulary Segmentation and Detection (2023) — 262 citations
• Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks (2024) — 1,209 citations
• DQ-DETR: Dual Query Detection Transformer for Phrase Extraction and Grounding (2022) — 45 citations