Ayan Kumar Bhunia

Computer Vision and Pattern Recognition · 2018–2022

A researcher in the Science corpus: 12 papers; trusts 1,009 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 363 citations into it; #6,355 of 21,575 by trust here, #7,340 by citations
• Artificial Intelligence — 219 citations into it; #26,890 of 35,345 by trust here, #22,463 by citations
• Representation & Self-Supervised Learning — 85 citations into it
• Model Architectures — 31 citations into it
• Generative Models — 29 citations into it
• Optimization & Training — 26 citations into it
• Multimodal Learning — 22 citations into it
• Natural Language Processing — 21 citations into it

Whom Ayan Kumar Bhunia 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. Yoshua Bengio
6. Karen Simonyan
7. Yann LeCun
8. Christian Szegedy
9. Ilya Sutskever
10. Ross B. Girshick

Who trusts Ayan Kumar Bhunia overall

1. Aneeshan Sain
2. Pinaki Nath Chowdhury
3. Tao A. Xiang
4. Yi-Zhe Song
5. Subhadeep Koley
6. Peng Xu
7. Yongxin Yang
8. Viswanatha Reddy Gajjala
9. Abdullah Faiz Ur Rahman Khilji
10. Timothy M. Hospedales

Papers from Ayan Kumar Bhunia's perspective

Ordered by how strongly the researchers Ayan Kumar Bhunia 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

• Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches (2020) — 375 citations
• Sketch Less for More: On-the-Fly Fine-Grained Sketch-Based Image Retrieval (2020) — 128 citations
• Cross-Modal Hierarchical Modelling for Fine-Grained Sketch Based Image Retrieval (2020) — 61 citations
• The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification (2020) — 371 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
• 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
• Cogni-Net: Cognitive Feature Learning Through Deep Visual Perception (2018) — 19 citations