Dingwen Zhang

Computer Vision and Pattern Recognition · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2014–2021

A researcher in the Science corpus: 15 papers; trusts 1,488 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 507 citations into it; #4,999 of 21,575 by trust here, #3,017 by citations
• Artificial Intelligence — 158 citations into it
• Cognitive Neuroscience — 75 citations into it
• Sensory Systems — 48 citations into it; #137 of 760 by trust here, #117 by citations
• Media Technology — 44 citations into it; #1,341 of 3,621 by trust here, #872 by citations
• Aerospace Engineering — 37 citations into it
• Representation & Self-Supervised Learning — 37 citations into it
• Model Architectures — 28 citations into it

Whom Dingwen Zhang trusts overall

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

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

Who trusts Dingwen Zhang overall

1. Gong Cheng
2. Junwei Han
3. Peicheng Zhou
4. Hao Chen
5. Wenguan Wang
6. Xiwen Yao
7. Deyu Meng
8. Yuan Yuan
9. Nian Liu
10. Lijun Zhao

Papers from Dingwen Zhang's perspective

Ordered by how strongly the researchers Dingwen 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. 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. 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

• A Self-Paced Multiple-Instance Learning Framework for Co-Saliency Detection (2015) — 136 citations
• Predicting eye fixations using convolutional neural networks (2015) — 249 citations
• Co-Saliency Detection via a Self-Paced Multiple-Instance Learning Framework (2017) — 484 citations
• Cosaliency Detection Based on Intrasaliency Prior Transfer and Deep Intersaliency Mining (2016) — 158 citations
• Salient Object Detection via Integrity Learning (2021) — 461 citations
• Weakly Supervised Object Localization and Detection: A Survey (2021) — 321 citations
• From Discriminant to Complete: Reinforcement Searching-Agent Learning for Weakly Supervised Object Detection (2020) — 53 citations
• Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey (2018) — 597 citations
• Revealing Event Saliency in Unconstrained Video Collection (2017) — 129 citations
• Detection of Co-salient Objects by Looking Deep and Wide (2016) — 323 citations
• Two-Stage Learning to Predict Human Eye Fixations via SDAEs (2016) — 150 citations
• Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning (2015)
• Background Prior-Based Salient Object Detection via Deep Reconstruction Residual (2015) — 449 citations
• Weakly Supervised Learning for Target Detection in Remote Sensing Images (2015) — 99 citations
• Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding (2014) — 215 citations