Jiemin Fang

Computer Vision and Pattern Recognition · 2019–2023

A researcher in the Science corpus: 8 papers; trusts 825 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 334 citations into it; #19,391 of 21,575 by trust here, #7,340 by citations
• Artificial Intelligence — 171 citations into it; #35,030 of 35,345 by trust here, #16,471 by citations
• Computer Graphics and Computer-Aided Design — 93 citations into it
• Model Architectures — 64 citations into it
• Optimization & Training — 42 citations into it
• Efficiency & Compression — 36 citations into it
• Evaluation & Benchmarks — 29 citations into it
• Representation & Self-Supervised Learning — 14 citations into it

Whom Jiemin Fang trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Ross B. Girshick
5. Karen Simonyan
6. Ilya Sutskever
7. Yoshua Bengio
8. Diederik P. Kingma
9. Christian Szegedy
10. Barret Zoph

Who trusts Jiemin Fang overall

1. Yuzhu Sun
2. Ziyi Yang
3. Pengzhen Ren
4. Qian Zhang
5. J. Lee
6. Yi-Hua Huang
7. Yiming Wang
8. Guanjun Wu
9. Wenyu Liu
10. Yuan Li

Papers from Jiemin Fang's perspective

Ordered by how strongly the researchers Jiemin Fang 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection (2021) — 416 citations
• Fast Dynamic Radiance Fields with Time-Aware Neural Voxels (2022) — 463 citations
• 4D Gaussian Splatting for Real-Time Dynamic Scene Rendering (2023) — 1,495 citations
• GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models (2023) — 319 citations
• ResizeMix: Mixing Data with Preserved Object Information and True Labels (2020) — 105 citations
• Fast Neural Network Adaptation via Parameter Remapping and Architecture Search (2020) — 36 citations
• Densely Connected Search Space for More Flexible Neural Architecture Search (2019) — 137 citations
• EAT-NAS: elastic architecture transfer for accelerating large-scale neural architecture search (2019) — 25 citations