James Sha

Computer Vision and Pattern Recognition · 2014–2015

A researcher in the Science corpus: 2 papers; trusts 10 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 29 citations into it; #10,886 of 21,575 by trust here, #9,435 by citations
• Aerospace Engineering — 18 citations into it; #2,424 of 6,421 by trust here, #1,853 by citations
• Computer Graphics and Computer-Aided Design — 11 citations into it
• Geology — 10 citations into it; #323 of 687 by trust here, #267 by citations
• 3D Vision & Pose — 9 citations into it; #394 of 689 by trust here, #392 by citations

Whom James Sha trusts overall

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

1. M. Kazhdan
2. Qian-Yi Zhou
3. Jan Smisek
4. William E. Lorensen
5. Yasutaka Furukawa
6. Michal Jancosek
7. Matthew Bolitho
8. Thomas Whelan
9. Arjun Singh
10. Richard A. Newcombe

Who trusts James Sha overall

1. Berk Çalli
2. Michael Firman
3. Arjun Singh
4. Marcus Gualtieri
5. Andreas ten Pas
6. Aaron Walsman
7. Debidatta Dwibedi
8. Ali Borji
9. Chaitanya Mitash
10. Ankur Handa

Papers from James Sha's perspective

Ordered by how strongly the researchers James Sha 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. ImageNet: A large-scale hierarchical image database
4. Indoor Segmentation and Support Inference from RGBD Images
5. Distinctive Image Features from Scale-Invariant Keypoints
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. International Journal of Computer Vision manuscript No. (will be inserted by the editor) The PASCAL Visual Object Classes (VOC) Challenge
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• BigBIRD: A large-scale 3D database of object instances (2014) — 378 citations
• Range sensor and silhouette fusion for high-quality 3D Scanning (2015) — 21 citations