Kevin Qinghong Lin

Computer Vision and Pattern Recognition · 2015–2024

A researcher in the Science corpus: 17 papers; trusts 1,461 researchers and is trusted by 88 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 703 citations into it; #3,376 of 21,575 by trust here, #1,732 by citations
• Artificial Intelligence — 484 citations into it; #15,810 of 35,345 by trust here, #2,863 by citations
• Multimodal Learning — 297 citations into it; #600 of 1,211 by trust here, #245 by citations
• Large Language Models — 114 citations into it
• Natural Language Processing — 106 citations into it
• Evaluation & Benchmarks — 97 citations into it
• Model Architectures — 74 citations into it
• Generative Models — 69 citations into it

Whom Kevin Qinghong Lin trusts overall

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

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

Who trusts Kevin Qinghong Lin overall

1. Shiv Ram Dubey
2. Georgios Pavlakos
3. Jingkuan Song
4. Shubham Goel
5. Wenhao Li
6. Yongjun Hong
7. T. Nakazawa
8. Ce Zheng
9. Fumin Shen
10. Cheng Deng

Papers from Kevin Qinghong Lin's perspective

Ordered by how strongly the researchers Kevin Qinghong Lin 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
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

• VIOLET : End-to-End Video-Language Transformers with Masked Visual-token Modeling (2021) — 255 citations
• GIT: A Generative Image-to-text Transformer for Vision and Language (2022) — 787 citations
• Adversarial Ranking for Language Generation (2017) — 350 citations
• MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action (2023) — 603 citations
• End-to-End Human Pose and Mesh Reconstruction with Transformers (2020) — 790 citations
• SwinBERT: End-to-End Transformers with Sparse Attention for Video Captioning (2021) — 341 citations
• The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision) (2023) — 973 citations
• MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities (2023) — 1,322 citations
• Egocentric Video-Language Pretraining (2022) — 295 citations
• VIVO: Surpassing Human Performance in Novel Object Captioning with Visual Vocabulary Pre-Training (2020) — 58 citations
• Deep learning of binary hash codes for fast image retrieval (2015) — 607 citations
• Mesh Graphormer (2021) — 419 citations
• Show-o: One Single Transformer to Unify Multimodal Understanding and Generation (2024) — 730 citations
• Text2Motion: from natural language instructions to feasible plans (2023) — 408 citations
• Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning (2023) — 519 citations
• ReCo: Region-Controlled Text-to-Image Generation (2022) — 214 citations
• Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks (2016) — 348 citations