Rahul Bhotika

Computer Vision and Pattern Recognition · 2019–2022

A researcher in the Science corpus: 7 papers; trusts 806 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 228 citations into it; #19,813 of 21,575 by trust here, #10,010 by citations
• Artificial Intelligence — 203 citations into it; #32,236 of 35,345 by trust here, #16,471 by citations
• Representation & Self-Supervised Learning — 73 citations into it
• Meta & Continual Learning — 40 citations into it
• Optimization & Training — 37 citations into it
• Evaluation & Benchmarks — 29 citations into it
• Model Architectures — 26 citations into it
• Efficiency & Compression — 18 citations into it

Whom Rahul Bhotika trusts overall

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

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

Who trusts Rahul Bhotika overall

1. Yonglong Tian
2. Qing Liu

Papers from Rahul Bhotika's perspective

Ordered by how strongly the researchers Rahul Bhotika 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. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Few-Shot Learning With Embedded Class Models and Shot-Free Meta Training (2019) — 192 citations
• X-DETR: A Versatile Architecture for Instance-wise Vision-Language Tasks (2022) — 52 citations
• Rethinking the Hyperparameters for Fine-tuning (2020) — 148 citations
• Class-Incremental Learning with Strong Pre-trained Models (2022) — 94 citations
• Incremental Meta-Learning via Indirect Discriminant Alignment (2020) — 6 citations
• Continual Universal Object Detection (2020) — 13 citations
• Multi-task incremental learning for object detection (2020) — 54 citations