Zhengjun Zha

Computer Vision and Pattern Recognition · 2012–2022

A researcher in the Science corpus: 27 papers; trusts 2,036 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 718 citations into it; #6,484 of 21,575 by trust here, #3,445 by citations
• Artificial Intelligence — 391 citations into it; #12,096 of 35,345 by trust here, #7,531 by citations
• Model Architectures — 95 citations into it
• Multimodal Learning — 93 citations into it; #925 of 1,211 by trust here, #802 by citations
• Natural Language Processing — 81 citations into it
• Representation & Self-Supervised Learning — 75 citations into it
• Media Technology — 68 citations into it
• Evaluation & Benchmarks — 52 citations into it

Whom Zhengjun Zha trusts overall

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

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

Who trusts Zhengjun Zha overall

1. Kai Zhu
2. Wei Zhai
3. Yang Cao
4. Van-Quang Nguyen
5. Sungjin Park
6. Kecheng Zheng
7. Kaiyang Zhou
8. Gen Luo
9. Ju He
10. Zhengyuan Yang

Papers from Zhengjun Zha's perspective

Ordered by how strongly the researchers Zhengjun Zha 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. 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

• Object Relational Graph With Teacher-Recommended Learning for Video Captioning (2020) — 316 citations
• Learning to Assemble Neural Module Tree Networks for Visual Grounding (2018) — 319 citations
• Learning Deep Bilinear Transformation for Fine-grained Image Representation (2019) — 178 citations
• Attribute-assisted reranking for web image retrieval (2012) — 50 citations
• Visual-Textual Joint Relevance Learning for Tag-Based Social Image Search (2013) — 463 citations
• Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-Grained Image Recognition (2019) — 436 citations
• Adaptive Transfer Network for Cross-Domain Person Re-Identification (2019) — 284 citations
• Learning Rich Part Hierarchies With Progressive Attention Networks for Fine-Grained Image Recognition (2020) — 111 citations
• Making History Matter: Gold-Critic Sequence Training for Visual Dialog (2019) — 8 citations
• Entity-Enhanced Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding (2022) — 76 citations
• Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning (2021) — 188 citations
• Self-Supervised Tuning for Few-Shot Segmentation (2020) — 30 citations
• Low-Rank Subspaces in GANs (2021) — 79 citations
• Robust (Semi) Nonnegative Graph Embedding (2014) — 58 citations
• Group-aware Label Transfer for Domain Adaptive Person Re-identification (2021) — 194 citations
• Image De-Raining Transformer (2022) — 372 citations
• Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning (2022) — 198 citations
• Image De-raining via Continual Learning (2021) — 66 citations
• Rethinking Graph Neural Architecture Search from Message-passing (2021) — 64 citations
• Light Field Super-Resolution with Zero-Shot Learning (2021) — 60 citations