Jenia Jitsev

Computer Vision and Pattern Recognition · 2021–2024

A researcher in the Science corpus: 6 papers; trusts 133 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 207 citations into it; #14,190 of 21,575 by trust here, #1,567 by citations
• Artificial Intelligence — 185 citations into it; #30,360 of 35,345 by trust here, #2,138 by citations

Whom Jenia Jitsev trusts

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

1. Alex Krizhevsky (in Computer Vision and Pattern Recognition)
2. Li Fei-Fei (in Computer Vision and Pattern Recognition)
3. Kaiming He (in Computer Vision and Pattern Recognition)
4. Ross B. Girshick (in Computer Vision and Pattern Recognition)
5. Andrew Zisserman (in Computer Vision and Pattern Recognition)
6. Ilya Sutskever (in Computer Vision and Pattern Recognition)
7. Shaoqing Ren (in Computer Vision and Pattern Recognition)
8. Jitendra Malik (in Computer Vision and Pattern Recognition)
9. Quoc V. Le (in Computer Vision and Pattern Recognition)
10. Geoffrey E. Hinton (in Computer Vision and Pattern Recognition)

Who trusts Jenia Jitsev

1. Ludwig Schmidt (in Computer Vision and Pattern Recognition)
2. Steven C. H. Hoi (in Computer Vision and Pattern Recognition)
3. Srivatsa Kundurthy (in Computer Vision and Pattern Recognition)
4. Ruslan Salakhutdinov (in Computer Vision and Pattern Recognition)
5. Yu Qiao (in Computer Vision and Pattern Recognition)

Papers from Jenia Jitsev's perspective

Ordered by how strongly the researchers Jenia Jitsev 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. Attention is All you Need (Neural Information Processing Systems)
4. ImageNet: A large-scale hierarchical image database
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet Large Scale Visual Recognition Challenge
7. Microsoft COCO: Common Objects in Context
8. Language Models are Few-Shot Learners (Neural Information Processing Systems)
9. Gradient-based learning applied to document recognition
10. Long Short-Term Memory

Their papers

• LAION-5B: An open large-scale dataset for training next generation image-text models (2022) — 5,389 citations
• LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs (2021) — 1,887 citations
• Reproducible Scaling Laws for Contrastive Language-Image Learning (2022) — 1,494 citations
• DataComp: In search of the next generation of multimodal datasets (2023) — 734 citations
• OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models (2023) — 642 citations
• DataComp-LM: In search of the next generation of training sets for language models (2024) — 374 citations