Jing Yu Koh

Computer Vision and Pattern Recognition · 2020–2024

A researcher in the Science corpus: 7 papers; trusts 869 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 275 citations into it; #14,421 of 21,575 by trust here, #2,562 by citations
• Artificial Intelligence — 220 citations into it
• Generative Models — 141 citations into it
• Multimodal Learning — 95 citations into it
• Large Language Models — 75 citations into it
• Natural Language Processing — 52 citations into it
• Evaluation & Benchmarks — 45 citations into it
• Representation & Self-Supervised Learning — 38 citations into it

Whom Jing Yu Koh trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Kaiming He
5. Yoshua Bengio
6. Ilya Sutskever
7. Yann LeCun
8. Jimmy Ba
9. Karen Simonyan
10. Ross B. Girshick

Who trusts Jing Yu Koh overall

1. Haotian Liu
2. Aditya Ramesh
3. Or Patashnik
4. Yihan Cao
5. Stanislav Frolov
6. Chunting Zhou
7. Ke Chen
8. Bin Lin
9. Quan Sun
10. Huiwen Chang

Papers from Jing Yu Koh's perspective

Ordered by how strongly the researchers Jing Yu Koh 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Scaling Autoregressive Models for Content-Rich Text-to-Image Generation (2022) — 1,537 citations
• Vector-quantized Image Modeling with Improved VQGAN (2021) — 809 citations
• Cross-Modal Contrastive Learning for Text-to-Image Generation (2021) — 451 citations
• Text-to-Image Generation Grounded by Fine-Grained User Attention (2020) — 65 citations
• Grounding Language Models to Images for Multimodal Generation (2023) — 98 citations
• VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks (2024) — 628 citations
• Generating Images with Multimodal Language Models (2023) — 384 citations