Richard Moore

Computer Vision and Pattern Recognition · with Blake · 2011–2013

A researcher in the Science corpus: 2 papers; trusts 24 researchers and is trusted by 469 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 50 citations into it; #1,955 of 21,575 by trust here, #1,294 by citations
• Artificial Intelligence — 9 citations into it
• Algorithms & Complexity — 7 citations into it
• Human-Computer Interaction — 6 citations into it; #241 of 2,253 by trust here, #7 by citations

Whom Richard Moore trusts

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

1. Jitendra Malik (in Computer Vision and Pattern Recognition)
2. Zhuowen Tu (in Computer Vision and Pattern Recognition)
3. Dariu M. Gavrila (in Computer Vision and Pattern Recognition)
4. Pedro F. Felzenszwalb (in Computer Vision and Pattern Recognition)
5. Andrew Zisserman (in Computer Vision and Pattern Recognition)
6. Greg Mori (in Computer Vision and Pattern Recognition)
7. Jamie Shotton (in Computer Vision and Pattern Recognition)
8. Ross B. Girshick (in Computer Vision and Pattern Recognition)
9. Lubomir D. Bourdev (in Computer Vision and Pattern Recognition)
10. Dan Huttenlocher (in Computer Vision and Pattern Recognition)

Who trusts Richard Moore

1. Luc Van Gool (in Computer Vision and Pattern Recognition)
2. Jitendra Malik (in Computer Vision and Pattern Recognition)
3. Andrew Zisserman (in Computer Vision and Pattern Recognition)
4. Cordelia Schmid (in Computer Vision and Pattern Recognition)
5. Kristen Grauman (in Computer Vision and Pattern Recognition)
6. Xiaogang Wang (in Computer Vision and Pattern Recognition)
7. Alan L. Yuille (in Computer Vision and Pattern Recognition)
8. Dacheng Tao (in Computer Vision and Pattern Recognition)
9. Bernt Schiele (in Computer Vision and Pattern Recognition)
10. Trevor Darrell (in Computer Vision and Pattern Recognition)

Papers from Richard Moore's perspective

Ordered by how strongly the researchers Richard Moore 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. Shape Matching and Object Recognition Using Shape Contexts
4. Pictorial Structures for Object Recognition
5. Poselets: Body part detectors trained using 3D human pose annotations
6. Random Forests
7. The Representation and Matching of Pictorial Structures
8. Gradient-based learning applied to document recognition
9. ImageNet: A large-scale hierarchical image database
10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers

• Real-time human pose recognition in parts from single depth images (2011) — 5,429 citations
• Efficient Human Pose Estimation from Single Depth Images (2013) — 462 citations