Tanmay Gupta

Computer Vision and Pattern Recognition · 2015–2024

A researcher in the Science corpus: 6 papers; trusts 907 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 217 citations into it; #5,327 of 21,575 by trust here, #6,206 by citations
• Artificial Intelligence — 175 citations into it; #11,662 of 35,345 by trust here, #10,430 by citations
• Multimodal Learning — 91 citations into it; #1,118 of 1,211 by trust here, #1,104 by citations
• Natural Language Processing — 47 citations into it
• Representation & Self-Supervised Learning — 34 citations into it
• Large Language Models — 27 citations into it
• Model Architectures — 26 citations into it
• Evaluation & Benchmarks — 19 citations into it

Whom Tanmay Gupta trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Yoshua Bengio
6. Ross B. Girshick
7. Ilya Sutskever
8. Yann LeCun
9. Ronald J. Williams
10. Shaoqing Ren

Who trusts Tanmay Gupta overall

1. Penghao Wu
2. Golnaz Ghiasi
3. Jiasen Lu
4. D'idac Sur'is
5. Haotian Liu
6. Christopher Clark
7. Weicheng Kuo
8. Yushi Hu
9. Pan Lu
10. Guowei Xu

Papers from Tanmay Gupta's perspective

Ordered by how strongly the researchers Tanmay Gupta 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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

• Completing 3D object shape from one depth image (2015) — 176 citations
• Visual Programming: Compositional visual reasoning without training (2022) — 718 citations
• Contrastive Learning for Weakly Supervised Phrase Grounding (2020) — 161 citations
• Towards General Purpose Vision Systems: An End-to-End Task-Agnostic Vision-Language Architecture (2021) — 58 citations
• Webly Supervised Concept Expansion for General Purpose Vision Models (2022) — 72 citations
• Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models (2024) — 410 citations