Shiming Xiang

Computer Vision and Pattern Recognition · 2007–2022

A researcher in the Science corpus: 24 papers; trusts 1,604 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 549 citations into it; #6,403 of 21,575 by trust here, #3,302 by citations
• Artificial Intelligence — 371 citations into it
• Model Architectures — 137 citations into it
• Computational Mechanics — 78 citations into it; #9,101 of 10,001 by trust here, #2,252 by citations
• Representation & Self-Supervised Learning — 74 citations into it; #2,459 of 3,145 by trust here, #2,330 by citations
• Optimization & Training — 65 citations into it
• Neural Architecture Search & AutoML — 58 citations into it
• Evaluation & Benchmarks — 41 citations into it

Whom Shiming Xiang trusts overall

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

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

Who trusts Shiming Xiang overall

1. Yuxin Peng
2. Marcus Chen
3. Chenping Hou
4. Jurgita Antucheviciene
5. Feiping Nie
6. Hongyun Cai
7. Oier Mees
8. Yi Yang
9. Jun Yue
10. Feng Xia

Papers from Shiming Xiang's perspective

Ordered by how strongly the researchers Shiming Xiang 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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 Adaptive Image Clustering (2017) — 553 citations
• Face Detection Based on Multi-Block LBP Representation (2007) — 445 citations
• Efficient Image Dehazing with Boundary Constraint and Contextual Regularization (2013) — 1,081 citations
• RENAS: Reinforced Evolutionary Neural Architecture Search (2019) — 126 citations
• Learning a Mahalanobis distance metric for data clustering and classification (2008) — 630 citations
• Gated Convolutional Neural Network for Semantic Segmentation in High-Resolution Images (2017) — 185 citations
• Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection (2018) — 130 citations
• You Only Search Once: Single Shot Neural Architecture Search via Direct Sparse Optimization (2018) — 100 citations
• Discriminative Least Squares Regression for Multiclass Classification and Feature Selection (2012) — 432 citations
• Learning Consistent Feature Representation for Cross-Modal Multimedia Retrieval (2015) — 213 citations
• Nonlinear Dimensionality Reduction with Local Spline Embedding (2009) — 116 citations
• Differentiable Architecture Search with Ensemble Gumbel-Softmax (2019) — 20 citations
• Expanding Language-Image Pretrained Models for General Video Recognition (2022) — 522 citations
• Spatio-Temporal Graph Structure Learning for Traffic Forecasting (2020) — 301 citations
• DATA: Differentiable ArchiTecture Approximation With Distribution Guided Sampling (2020) — 15 citations
• Relation-Shape Convolutional Neural Network for Point Cloud Analysis (2019) — 1,032 citations
• AugFPN: Improving Multi-Scale Feature Learning for Object Detection (2019) — 495 citations
• DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing (2019) — 299 citations
• DATA: Differentiable ArchiTecture Approximation (2019) — 56 citations
• Automatic Road Detection and Centerline Extraction via Cascaded End-to-End Convolutional Neural Network (2017) — 386 citations