Yao Yao

Computer Vision and Pattern Recognition · International Journal of Geographical Information Science · 2017–2021

A researcher in the Science corpus: 6 papers; trusts 8 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 34 citations into it
• Artificial Intelligence — 26 citations into it
• Information Systems — 9 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Yao Yao trusts overall

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

1. Peter J. Rousseeuw
2. Gérard Biau
3. Evan Shelhamer
4. David M. Blei
5. Mo Yu
6. Yu Zheng
7. Bolei Zhou
8. Tarn Duong
9. Geoffrey E. Hinton
10. Andrew Y. Ng

Who trusts Yao Yao overall

1. Filip Biljecki
2. Koichi Ito
3. Xiaoping Liu

Papers from Yao Yao's perspective

Ordered by how strongly the researchers Yao Yao 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Analyzing the correlation between visual space and residents' psychology in Wuhan, China using street-view images and deep-learning technique (2021) — 130 citations
• A human-machine adversarial scoring framework for urban perception assessment using street-view images (2019) — 384 citations
• The relationship between visual enclosure for neighbourhood street walkability and elders’ mental health in China: Using street view images (2019) — 190 citations
• Delineating multi-scenario urban growth boundaries with a CA-based FLUS model and morphological method (2018) — 441 citations
• Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model (2017) — 458 citations
• Classifying urban land use by integrating remote sensing and social media data (2017) — 328 citations