David Picard

Computer Vision and Pattern Recognition · 2016–2022

A researcher in the Science corpus: 5 papers; trusts 818 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 163 citations into it; #8,910 of 21,575 by trust here, #9,563 by citations
• Artificial Intelligence — 68 citations into it; #15,366 of 35,345 by trust here, #12,387 by citations
• Human-Computer Interaction — 48 citations into it
• Biomedical Engineering — 33 citations into it
• Meta & Continual Learning — 27 citations into it
• 3D Vision & Pose — 20 citations into it; #406 of 689 by trust here, #529 by citations
• Model Architectures — 16 citations into it
• Representation & Self-Supervised Learning — 13 citations into it

Whom David Picard trusts overall

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

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

Who trusts David Picard overall

1. Ye Yuan
2. Yucheng Chen
3. Ce Zheng
4. Jun Liu
5. Diogo Carbonera Luvizon
6. Donglai Xiang
7. Jiefeng Li
8. Dario Pavllo
9. Stefano Savazzi
10. Yuanming Shi

Papers from David Picard's perspective

Ordered by how strongly the researchers David Picard 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. Gradient-based learning applied to document recognition
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• 2D/3D Pose Estimation and Action Recognition Using Multitask Deep Learning (2018) — 535 citations
• Human Pose Regression by Combining Indirect Part Detection and Contextual Information (2017) — 268 citations
• Gossip training for deep learning (2016) — 124 citations
• Learning features combination for human action recognition from skeleton sequences (2017) — 108 citations
• FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learning (2022) — 201 citations