Manoj Acharya

Computer Vision and Pattern Recognition · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 571 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 113 citations into it; #10,165 of 21,575 by trust here, #7,938 by citations
• Artificial Intelligence — 111 citations into it; #15,145 of 35,345 by trust here, #11,327 by citations
• Meta & Continual Learning — 33 citations into it; #485 of 1,041 by trust here, #369 by citations
• Representation & Self-Supervised Learning — 27 citations into it
• Multimodal Learning — 21 citations into it
• Cognitive Neuroscience — 16 citations into it
• Natural Language Processing — 13 citations into it
• Evaluation & Benchmarks — 11 citations into it

Whom Manoj Acharya trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Kushal Kafle
4. Shaoqing Ren
5. Ross B. Girshick
6. Alex Krizhevsky
7. Yoshua Bengio
8. Yann LeCun
9. Diederik P. Kingma
10. Ilya Sutskever

Who trusts Manoj Acharya overall

1. Tyler L. Hayes
2. Kushal Kafle
3. Christopher Kanan
4. Dave Zhenyu Chen
5. Robik Shrestha
6. Zheda Mai
7. Liu Yu-yang
8. Junsu Kim
9. Jhair Gallardo
10. Angel X. Chang

Papers from Manoj Acharya's perspective

Ordered by how strongly the researchers Manoj Acharya 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. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
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

• REMIND Your Neural Network to Prevent Catastrophic Forgetting (2019) — 349 citations
• RODEO: Replay for Online Object Detection (2020) — 52 citations
• VQD: Visual Query Detection In Natural Scenes (2019) — 19 citations