Anoop Cherian

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 8 papers; trusts 1,097 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 219 citations into it; #8,384 of 21,575 by trust here, #7,852 by citations
• Artificial Intelligence — 140 citations into it; #13,888 of 35,345 by trust here, #18,186 by citations
• Biomedical Engineering — 31 citations into it
• Evaluation & Benchmarks — 29 citations into it
• Representation & Self-Supervised Learning — 19 citations into it
• Model Architectures — 18 citations into it
• Human-Computer Interaction — 17 citations into it
• Endocrinology, Diabetes and Metabolism — 11 citations into it

Whom Anoop Cherian trusts overall

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

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

Who trusts Anoop Cherian overall

1. Jue Wang
2. Yi Zhu
3. Pichao Wang
4. Fatih Murat Porikli
5. Laura Sevilla-Lara
6. Zhiyuan Shi
7. Piotr Koniusz
8. Junwu Weng
9. Stephen Gould
10. Zhenzhong Lan

Papers from Anoop Cherian's perspective

Ordered by how strongly the researchers Anoop Cherian trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
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

• Audio Visual Scene-Aware Dialog (2019) — 230 citations
• DeepPermNet: Visual Permutation Learning (2017) — 109 citations
• End-to-end Audio Visual Scene-aware Dialog Using Multimodal Attention-based Video Features (2018) — 132 citations
• Generalized Rank Pooling for Activity Recognition (2017) — 87 citations
• Ordered Pooling of Optical Flow Sequences for Action Recognition (2017) — 47 citations
• Video Representation Learning Using Discriminative Pooling (2018) — 63 citations
• On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimization (2016) — 249 citations
• Tensor Representations via Kernel Linearization for Action Recognition from 3D Skeletons (2016) — 118 citations