Tao Xu

Computer Vision and Pattern Recognition · with Zhang · 2012–2020

A researcher in the Science corpus: 8 papers; trusts 751 researchers and is trusted by 716 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 245 citations into it; #1,619 of 21,575 by trust here, #909 by citations
• Generative Models — 119 citations into it; #198 of 1,956 by trust here, #125 by citations
• Artificial Intelligence — 107 citations into it
• Model Architectures — 32 citations into it
• Optimization & Training — 17 citations into it
• Evaluation & Benchmarks — 16 citations into it
• Media Technology — 16 citations into it; #1,399 of 3,621 by trust here, #1,135 by citations
• Multimodal Learning — 13 citations into it; #972 of 1,211 by trust here, #302 by citations

Whom Tao Xu trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Kaiming He
5. Yoshua Bengio
6. Scott E. Reed
7. Ross B. Girshick
8. Yann LeCun
9. Christian Szegedy
10. Ilya Sutskever

Who trusts Tao Xu overall

1. Jiadong Liang
2. Cristian Bodnar
3. Mingkuan Yuan
4. Han Zhang
5. Weihao Xia
6. Tobias Hinz
7. Pengchuan Zhang
8. Jaemin Cho
9. Ming Tao
10. Tianfu Wu

Papers from Tao Xu's perspective

Ordered by how strongly the researchers Tao Xu 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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

• StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks (2016) — 2,951 citations
• AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks (2017) — 1,976 citations
• StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks (2017) — 1,190 citations
• SPDA-CNN: Unifying Semantic Part Detection and Abstraction for Fine-Grained Recognition (2016) — 297 citations
• FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions (2020) — 334 citations
• SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation (2017) — 601 citations
• On the Discrimination-Generalization Tradeoff in GANs (2017) — 110 citations
• High-resolution satellite scene classification using a sparse coding based multiple feature combination (2012) — 305 citations