Pinaki Nath Chowdhury

Computer Vision and Pattern Recognition · 2021–2022

A researcher in the Science corpus: 5 papers; trusts 540 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 177 citations into it; #11,934 of 21,575 by trust here, #12,689 by citations
• Artificial Intelligence — 86 citations into it; #25,113 of 35,345 by trust here, #25,809 by citations
• Representation & Self-Supervised Learning — 45 citations into it
• Optimization & Training — 13 citations into it
• Reinforcement Learning — 7 citations into it
• Control and Systems Engineering — 6 citations into it
• Information Retrieval & Search — 6 citations into it
• Signal Processing — 6 citations into it

Whom Pinaki Nath Chowdhury trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Ayan Kumar Bhunia
6. Yoshua Bengio
7. Yi-Zhe Song
8. Jifei Song
9. Christian Szegedy
10. Yann LeCun

Who trusts Pinaki Nath Chowdhury overall

1. Ayan Kumar Bhunia
2. Aneeshan Sain
3. Subhadeep Koley
4. Tao A. Xiang
5. Yi-Zhe Song

Papers from Pinaki Nath Chowdhury's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Partially Does It: Towards Scene-Level FG-SBIR with Partial Input (2022) — 31 citations
• Sketch3T: Test-Time Training for Zero-Shot SBIR (2022) — 71 citations
• Sketching without Worrying: Noise-Tolerant Sketch-Based Image Retrieval (2022) — 58 citations
• Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting (2021) — 69 citations
• More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval (2021) — 70 citations