Tsu-Jui Fu
Computer Vision and Pattern Recognition · 2018–2023
A researcher in the Science corpus: 8 papers; trusts 1,018 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 265 citations into it; #18,753 of 21,575 by trust here, #8,021 by citations
- Artificial Intelligence — 174 citations into it; #24,347 of 35,345 by trust here, #16,166 by citations
- Multimodal Learning — 81 citations into it
- Generative Models — 68 citations into it
- Natural Language Processing — 58 citations into it; #4,087 of 5,688 by trust here, #5,412 by citations
- Representation & Self-Supervised Learning — 38 citations into it
- Model Architectures — 30 citations into it
- Evaluation & Benchmarks — 24 citations into it
Whom Tsu-Jui Fu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Kaiming He
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yoshua Bengio
- Ilya Sutskever
- Sepp Hochreiter
- Jimmy Ba
- Ronald J. Williams
- Yann LeCun
Who trusts Tsu-Jui Fu overall
- Zexuan Zhong
- Jin Tao
- Yi Wang
- Ciyuan Peng
- Ying Lin
- Lingfeng Zhong
- Zhepei Wei
- Renqian Luo
- Danqi Chen
- Antoine Yang
Papers from Tsu-Jui Fu's perspective
Ordered by how strongly the researchers Tsu-Jui Fu trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- VIOLET : End-to-End Video-Language Transformers with Masked Visual-token Modeling (2021) — 255 citations
- Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis (2022) — 441 citations
- Dynamic Video Segmentation Network (2018) — 143 citations
- Counterfactual Vision-and-Language Navigation via Adversarial Path Sampling (2019) — 47 citations
- Speed Reading: Learning to Read ForBackward via Shuttle (2018) — 17 citations
- LayoutGPT: Compositional Visual Planning and Generation with Large Language Models (2023) — 405 citations
- Language-Driven Image Style Transfer (2021) — 11 citations
- GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction (2019) — 386 citations