Mandela Patrick

Computer Vision and Pattern Recognition · 2019–2021

A researcher in the Science corpus: 5 papers; trusts 1,078 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 310 citations into it
• Artificial Intelligence — 220 citations into it; #9,439 of 35,345 by trust here, #9,369 by citations
• Representation & Self-Supervised Learning — 126 citations into it
• Multimodal Learning — 94 citations into it
• Natural Language Processing — 47 citations into it
• Evaluation & Benchmarks — 36 citations into it
• Model Architectures — 36 citations into it
• Signal Processing — 34 citations into it

Whom Mandela Patrick trusts overall

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

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

Who trusts Mandela Patrick overall

1. Po-Yao (Bernie) Huang
2. Hila Chefer
3. Shir Gur
4. Yuying Ge
5. Rohit Girdhar
6. Ho Kei Cheng
7. Wojciech Samek
8. Sang-Min Park
9. Yixiao Ge
10. Kunchang Li

Papers from Mandela Patrick's perspective

Ordered by how strongly the researchers Mandela Patrick 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
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

• Support-set bottlenecks for video-text representation learning (2020) — 271 citations
• Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models (2021) — 64 citations
• Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers (2021) — 372 citations
• Understanding Deep Networks via Extremal Perturbations and Smooth Masks (2019) — 494 citations
• Multi-modal Self-Supervision from Generalized Data Transformations (2020) — 196 citations