Abhishek Kar
Computer Vision and Pattern Recognition · 2014–2019
A researcher in the Science corpus: 6 papers; trusts 801 researchers and is trusted by 743 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 253 citations into it; #1,602 of 21,575 by trust here, #3,124 by citations
- Aerospace Engineering — 81 citations into it; #1,341 of 6,421 by trust here, #1,237 by citations
- 3D Vision & Pose — 53 citations into it; #165 of 689 by trust here, #146 by citations
- Computational Mechanics — 40 citations into it; #2,770 of 10,001 by trust here, #1,711 by citations
- Computer Graphics and Computer-Aided Design — 35 citations into it; #482 of 1,584 by trust here, #398 by citations
- Artificial Intelligence — 24 citations into it
- Evaluation & Benchmarks — 20 citations into it
- Media Technology — 17 citations into it
Whom Abhishek Kar trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jitendra Malik
- Alex Krizhevsky
- Geoffrey E. Hinton
- Ross B. Girshick
- Yann LeCun
- Kaiming He
- Ilya Sutskever
- Yoshua Bengio
- Diederik P. Kingma
- Jianguo Zhang
Who trusts Abhishek Kar overall
- Jiajun Wu
- Jitendra Malik
- Angjoo Kanazawa
- Shikun Liu
- Yao Yao
- Bo Yang
- Maxim Tatarchenko
- Shubham Tulsiani
- David Novotný
- Chen Kong
Papers from Abhishek Kar's perspective
Ordered by how strongly the researchers Abhishek Kar trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Category-specific object reconstruction from a single image (2014) — 295 citations
- Learning a Multi-View Stereo Machine (2017) — 585 citations
- Local light field fusion (2019) — 1,308 citations
- Amodal Completion and Size Constancy in Natural Scenes (2015) — 66 citations
- Learning Category-Specific Deformable 3D Models for Object Reconstruction (2017) — 79 citations
- The three R's of computer vision: Recognition, reconstruction and reorganization (2016) — 52 citations