Leonid Karlinsky

Computer Vision and Pattern Recognition · 2012–2022

A researcher in the Science corpus: 11 papers; trusts 1,022 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 379 citations into it; #6,915 of 21,575 by trust here, #4,834 by citations
• Artificial Intelligence — 342 citations into it; #25,890 of 35,345 by trust here, #6,911 by citations
• Representation & Self-Supervised Learning — 125 citations into it; #2,893 of 3,145 by trust here, #1,582 by citations
• Meta & Continual Learning — 76 citations into it
• Model Architectures — 53 citations into it
• Radiology, Nuclear Medicine and Imaging — 49 citations into it
• Evaluation & Benchmarks — 47 citations into it
• Generative Models — 27 citations into it

Whom Leonid Karlinsky trusts overall

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

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

Who trusts Leonid Karlinsky overall

1. Xianjie Chen
2. Zhe Cao
3. Guanghan Ning
4. Varun Ramakrishna
5. Yu-Xiong Wang
6. Alan L. Yuille
7. Shih-En Wei
8. Martial Hebert
9. Qi Fan
10. Zhi Zhang

Papers from Leonid Karlinsky's perspective

Ordered by how strongly the researchers Leonid Karlinsky 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
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

• Delta-encoder: an effective sample synthesis method for few-shot object recognition (2018) — 387 citations
• Using Linking Features in Learning Non-parametric Part Models (2012) — 53 citations
• RepMet: Representative-Based Metric Learning for Classification and Few-Shot Object Detection (2018) — 355 citations
• AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition (2021) — 70 citations
• AR-Net: Adaptive Frame Resolution for Efficient Action Recognition (2020) — 177 citations
• A Broader Study of Cross-Domain Few-Shot Learning (2019) — 423 citations
• Co-regularized Alignment for Unsupervised Domain Adaptation (2018) — 167 citations
• A New Benchmark for Evaluation of Cross-Domain Few-Shot Learning (2019) — 45 citations
• CODA-Prompt: COntinual Decomposed Attention-Based Prompting for Rehearsal-Free Continual Learning (2022) — 609 citations
• Baby steps towards few-shot learning with multiple semantics (2019) — 116 citations
• RepMet: Representative-based metric learning for classification and one-shot object detection (2018) — 39 citations