Yongdong Zhang
Computer Vision and Pattern Recognition · 2009–2021
A researcher in the Science corpus: 31 papers; trusts 1,807 researchers and is trusted by 52 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 563 citations into it; #4,529 of 21,575 by trust here, #2,055 by citations
- Artificial Intelligence — 322 citations into it; #11,722 of 35,345 by trust here, #6,378 by citations
- Model Architectures — 83 citations into it
- Natural Language Processing — 77 citations into it
- Multimodal Learning — 75 citations into it; #1,063 of 1,211 by trust here, #935 by citations
- Information Systems — 66 citations into it; #1,482 of 8,771 by trust here, #1,203 by citations
- Molecular Biology — 65 citations into it
- Representation & Self-Supervised Learning — 52 citations into it
Whom Yongdong Zhang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Geoffrey E. Hinton
- Ross B. Girshick
- Diederik P. Kingma
- Karen Simonyan
- Ilya Sutskever
- Yoshua Bengio
- Yann LeCun
- Andrew Zisserman
Who trusts Yongdong Zhang overall
- Kai Shu
- Xinyi Zhou
- Xinyi Zhou
- Zhiwei Jin
- Shaoxiang Chen
- Nicholas Carlini
- Reza Zafarani
- Junliang Yu
- Yuxin Peng
- Xiang Wang
Papers from Yongdong Zhang's perspective
Ordered by how strongly the researchers Yongdong Zhang trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Multi-task deep visual-semantic embedding for video thumbnail selection (2015) — 255 citations
- Deep Learning for Content-Based Image Retrieval: A Comprehensive Study (2014) — 938 citations
- APE-GAN: Adversarial Perturbation Elimination with GAN (2017) — 245 citations
- Deep Representation Learning With Part Loss for Person Re-Identification (2017) — 433 citations
- Scale-Adaptive Convolutions for Scene Parsing (2017) — 196 citations
- CGNet: A Light-Weight Context Guided Network for Semantic Segmentation (2018) — 786 citations
- Learning Multimodal Attention LSTM Networks for Video Captioning (2017) — 159 citations
- Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition (2021) — 416 citations
- Multiview Spectral Embedding (2010) — 513 citations
- Coarse-to-Fine Description for Fine-Grained Visual Categorization (2016) — 76 citations
- Multi-Modality Cross Attention Network for Image and Sentence Matching (2020) — 451 citations
- News Verification by Exploiting Conflicting Social Viewpoints in Microblogs (2016) — 396 citations
- Novel Visual and Statistical Image Features for Microblogs News Verification (2017) — 417 citations
- Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction (2019) — 501 citations
- Causal Intervention for Leveraging Popularity Bias in Recommendation (2021) — 535 citations
- WBSMDA: Within and Between Score for MiRNA-Disease Association prediction (2016) — 346 citations
- Drug-target interaction prediction: databases, web servers and computational models (2016) — 554 citations
- Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer (2021) — 401 citations
- Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection (2021) — 345 citations
- STAT: Spatial-Temporal Attention Mechanism for Video Captioning (2020) — 376 citations