Haochen Wang

Computer Vision and Pattern Recognition · 2019–2022

A researcher in the Science corpus: 6 papers; trusts 495 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 156 citations into it; #20,737 of 21,575 by trust here, #7,421 by citations
• Artificial Intelligence — 66 citations into it
• Generative Models — 24 citations into it
• Representation & Self-Supervised Learning — 24 citations into it
• Computational Mechanics — 21 citations into it; #9,578 of 10,001 by trust here, #3,763 by citations
• Computer Graphics and Computer-Aided Design — 21 citations into it; #1,525 of 1,584 by trust here, #711 by citations
• Model Architectures — 12 citations into it
• Optimization & Training — 12 citations into it

Whom Haochen Wang trusts overall

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

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

Who trusts Haochen Wang overall

1. Peng Wang
2. Ho Kei Cheng
3. Heewoo Jun
4. Ruoshi Liu
5. Rundi Wu
6. Juhong Min
7. Axel Sauer
8. Yuan Liu
9. Jiaxiang Tang
10. Minghua Liu

Papers from Haochen Wang's perspective

Ordered by how strongly the researchers Haochen Wang 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

• Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation (2022) — 701 citations
• Seeing through Sounds: Predicting Visual Semantic Segmentation Results from Multichannel Audio Signals (2019) — 16 citations
• Few-Shot Semantic Segmentation with Democratic Attention Networks (2020) — 234 citations
• DIODE: A Dense Indoor and Outdoor DEpth Dataset (2019) — 365 citations
• SwiftNet: Real-time Video Object Segmentation (2021) — 187 citations
• Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels (2022) — 511 citations