Cheng Deng

Computer Vision and Pattern Recognition · 2017–2022

A researcher in the Science corpus: 17 papers; trusts 1,684 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 421 citations into it; #6,029 of 21,575 by trust here, #3,558 by citations
• Artificial Intelligence — 225 citations into it; #18,876 of 35,345 by trust here, #8,706 by citations
• Representation & Self-Supervised Learning — 107 citations into it
• Signal Processing — 34 citations into it
• Optimization & Training — 28 citations into it
• Multimodal Learning — 26 citations into it
• Meta & Continual Learning — 25 citations into it
• Model Architectures — 25 citations into it

Whom Cheng Deng trusts overall

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

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

Who trusts Cheng Deng overall

1. Xu Yang
2. Shiv Ram Dubey
3. Yanan Gu
4. Kun-Juan Wei
5. Erkun Yang
6. Zhedong Zheng
7. Xiaoqiang Yan
8. Hengtong Hu
9. Tongliang Liu
10. Chen Chen

Papers from Cheng Deng's perspective

Ordered by how strongly the researchers Cheng Deng 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
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

• Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization (2017) — 580 citations
• Multi-Task Collaborative Network for Joint Referring Expression Comprehension and Segmentation (2020) — 370 citations
• Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training (2018) — 392 citations
• Triplet-Based Deep Hashing Network for Cross-Modal Retrieval (2018) — 371 citations
• Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval (2018) — 410 citations
• Adversarial Fine-Grained Composition Learning for Unseen Attribute-Object Recognition (2019) — 96 citations
• Class-Incremental Instance Segmentation via Multi-Teacher Networks (2021) — 32 citations
• Pairwise Relationship Guided Deep Hashing for Cross-Modal Retrieval (2017) — 336 citations
• Not Just Selection, but Exploration: Online Class-Incremental Continual Learning via Dual View Consistency (2022) — 106 citations
• Two-Stream Deep Hashing With Class-Specific Centers for Supervised Image Search (2020) — 83 citations
• Multi-Scale Fusion Subspace Clustering Using Similarity Constraint (2020) — 41 citations
• Heterogeneous Graph Attention Network for Unsupervised Multiple-Target Domain Adaptation (2020) — 153 citations
• Lifelong Zero-Shot Learning (2020) — 74 citations
• Saliency Detection via a Multiple Self-Weighted Graph-Based Manifold Ranking (2020) — 54 citations
• Unsupervised Semantic-Preserving Adversarial Hashing for Image Search (2019) — 150 citations
• Deep Spectral Clustering Using Dual Autoencoder Network (2019) — 288 citations
• Shared Predictive Cross-Modal Deep Quantization (2018) — 137 citations