Aneeshan Sain

Computer Vision and Pattern Recognition · 2020–2022

A researcher in the Science corpus: 7 papers; trusts 656 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 239 citations into it; #9,050 of 21,575 by trust here, #10,500 by citations
• Artificial Intelligence — 151 citations into it
• Representation & Self-Supervised Learning — 61 citations into it
• Generative Models — 18 citations into it
• Optimization & Training — 17 citations into it
• Natural Language Processing — 16 citations into it
• Aerospace Engineering — 11 citations into it
• Model Architectures — 11 citations into it

Whom Aneeshan Sain trusts overall

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

1. Geoffrey E. Hinton
2. David G. Lowe
3. Yi-Zhe Song
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Yoshua Bengio
7. Kaiming He
8. Jifei Song
9. Ayan Kumar Bhunia
10. Ronald J. Williams

Who trusts Aneeshan Sain overall

1. Ayan Kumar Bhunia
2. Pinaki Nath Chowdhury
3. Tao A. Xiang
4. Yi-Zhe Song
5. Subhadeep Koley
6. Yongxin Yang
7. Viswanatha Reddy Gajjala
8. Abdullah Faiz Ur Rahman Khilji

Papers from Aneeshan Sain's perspective

Ordered by how strongly the researchers Aneeshan Sain 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

• Cross-Modal Hierarchical Modelling for Fine-Grained Sketch Based Image Retrieval (2020) — 61 citations
• Doodle It Yourself: Class Incremental Learning by Drawing a Few Sketches (2022) — 33 citations
• 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
• StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval (2021) — 109 citations
• More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval (2021) — 70 citations