Abhijit Kundu

Computer Vision and Pattern Recognition · 2014–2022

A researcher in the Science corpus: 5 papers; trusts 774 researchers and is trusted by 150 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 193 citations into it; #2,927 of 21,575 by trust here, #5,464 by citations
• Artificial Intelligence — 45 citations into it
• Aerospace Engineering — 42 citations into it; #1,111 of 6,421 by trust here, #1,268 by citations
• Computational Mechanics — 37 citations into it
• Computer Graphics and Computer-Aided Design — 35 citations into it
• Evaluation & Benchmarks — 31 citations into it
• 3D Vision & Pose — 27 citations into it
• Model Architectures — 15 citations into it

Whom Abhijit Kundu trusts overall

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

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

Who trusts Abhijit Kundu overall

1. Fisher Yu
2. Joel Janai
3. Shichen Liu
4. Fabian Manhardt
5. Fahad Lateef
6. Ramin Nabati
7. David Nilsson
8. Jitendra Malik
9. Georgia Gkioxari
10. Xingyi Zhou

Papers from Abhijit Kundu's perspective

Ordered by how strongly the researchers Abhijit Kundu 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. 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

• Joint Semantic Segmentation and 3D Reconstruction from Monocular Video (2014) — 254 citations
• 3D-RCNN: Instance-Level 3D Object Reconstruction via Render-and-Compare (2018) — 359 citations
• Feature Space Optimization for Semantic Video Segmentation (2016) — 194 citations
• Kubric: A scalable dataset generator (2022) — 467 citations
• Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation (2022) — 330 citations