Brian C. Lovell

Computer Vision and Pattern Recognition · CVPR 2011 WORKSHOPS · 1994–2022

A researcher in the Science corpus: 11 papers; trusts 680 researchers and is trusted by 80 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 132 citations into it; #3,280 of 21,575 by trust here, #4,067 by citations
• Artificial Intelligence — 93 citations into it; #5,952 of 35,345 by trust here, #7,728 by citations
• Representation & Self-Supervised Learning — 43 citations into it; #1,037 of 3,145 by trust here, #1,507 by citations
• Signal Processing — 28 citations into it; #7,065 of 7,572 by trust here, #4,900 by citations
• Meta & Continual Learning — 25 citations into it; #477 of 1,041 by trust here, #681 by citations
• Evaluation & Benchmarks — 12 citations into it
• Model Architectures — 11 citations into it
• Efficiency & Compression — 10 citations into it

Whom Brian C. Lovell trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Ross B. Girshick
4. Alex Krizhevsky
5. Yoshua Bengio
6. Radford M. Neal
7. Shaoqing Ren
8. Yann LeCun
9. Diederik P. Kingma
10. Peter N. Belhumeur

Who trusts Brian C. Lovell overall

1. Wouter M. Kouw
2. Conrad Sanderson
3. Yuezun Li
4. Jon Gauthier
5. Jaume Amores
6. Gabriella Csurka
7. Muhammad Ghifary
8. Mehrtash Harandi
9. Wen Li
10. Zhiwu Huang

Papers from Brian C. Lovell's perspective

Ordered by how strongly the researchers Brian C. Lovell 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. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Unsupervised Domain Adaptation by Domain Invariant Projection (2013) — 462 citations
• Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference (2009) — 334 citations
• Domain Adaptation on the Statistical Manifold (2014) — 140 citations
• Sparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approach (2012) — 211 citations
• Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching (2011) — 308 citations
• Few-Shot Class-Incremental Learning from an Open-Set Perspective (2022) — 151 citations
• Faster ILOD: Incremental Learning for Object Detectors based on Faster RCNN (2020) — 148 citations
• SID: Incremental Learning for Anchor-Free Object Detection via Selective and Inter-Related Distillation (2020) — 68 citations
• Face Recognition on Consumer Devices: Reflections on Replay Attacks (2015) — 135 citations
• Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition (2011) — 340 citations
• Classification of cervical cell nuclei using morphological segmentation and textural feature extraction (1994)