Krishna Kumar Singh

Computer Vision and Pattern Recognition · 2016–2023

A researcher in the Science corpus: 9 papers; trusts 1,746 researchers and is trusted by 293 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 353 citations into it; #2,320 of 21,575 by trust here, #4,690 by citations
• Generative Models — 136 citations into it; #1,451 of 1,956 by trust here, #1,460 by citations
• Artificial Intelligence — 110 citations into it; #5,849 of 35,345 by trust here, #12,974 by citations
• Representation & Self-Supervised Learning — 41 citations into it; #2,901 of 3,145 by trust here, #2,217 by citations
• Evaluation & Benchmarks — 29 citations into it; #2,067 of 3,147 by trust here, #2,545 by citations
• Computer Graphics and Computer-Aided Design — 24 citations into it
• Multimodal Learning — 23 citations into it
• Media Technology — 19 citations into it

Whom Krishna Kumar Singh trusts overall

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

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

Who trusts Krishna Kumar Singh overall

1. Xiaolin Zhang
2. Yunchao Wei
3. Junsuk Choe
4. Yuheng Li
5. Prateek Katiyar
6. Tao Hu
7. Senthil Purushwalkam
8. Phuc Xuan Nguyen
9. Zhengyuan Yang
10. Yong Jae Lee

Papers from Krishna Kumar Singh's perspective

Ordered by how strongly the researchers Krishna Kumar Singh 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

• Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-Supervised Object and Action Localization (2017) — 739 citations
• Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection (2016) — 68 citations
• Zero-shot Image-to-Image Translation (2023) — 653 citations
• MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation (2019) — 84 citations
• KrishnaCam: Using a longitudinal, single-person, egocentric dataset for scene understanding tasks (2016) — 73 citations
• Collaging Class-specific GANs for Semantic Image Synthesis (2021) — 36 citations
• FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery (2018) — 141 citations
• DOCK: Detecting Objects by Transferring Common-Sense Knowledge (2018) — 31 citations
• Contrastive Learning for Diverse Disentangled Foreground Generation (2022) — 8 citations