Robert B. Fisher

Computer Vision and Pattern Recognition · 2003–2021

A researcher in the Science corpus: 5 papers; trusts 242 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 45 citations into it; #7,089 of 21,575 by trust here, #11,083 by citations
• Artificial Intelligence — 31 citations into it; #10,354 of 35,345 by trust here, #16,471 by citations
• Representation & Self-Supervised Learning — 16 citations into it
• Generative Models — 9 citations into it
• Cognitive Neuroscience — 5 citations into it
• Model Architectures — 5 citations into it

Whom Robert B. Fisher trusts overall

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

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

Who trusts Robert B. Fisher overall

1. Ali Borji
2. Chenlei Guo
3. Magdalini Paschali
4. Simone Frintrop
5. Yuexiang Li
6. Jeremy Kawahara
7. Louis Kratz
8. Laurent Itti
9. Liming Zhang

Papers from Robert B. Fisher's perspective

Ordered by how strongly the researchers Robert B. Fisher 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Going deeper with convolutions
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. ImageNet: A large-scale hierarchical image database

Their papers

• Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views (2021) — 62 citations
• Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches (2016) — 41 citations
• A Color and Texture Based Hierarchical K-NN Approach to the Classification of Non-melanoma Skin Lesions (2013) — 307 citations
• Modelling Crowd Scenes for Event Detection (2006) — 308 citations
• Object-based visual attention for computer vision (2003) — 346 citations