Baoyuan Wu

Computer Vision and Pattern Recognition · 2018–2023

A researcher in the Science corpus: 16 papers; trusts 1,474 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 498 citations into it; #11,475 of 21,575 by trust here, #4,067 by citations
• Artificial Intelligence — 290 citations into it; #17,795 of 35,345 by trust here, #7,531 by citations
• Safety, Robustness & Interpretability — 115 citations into it
• Generative Models — 93 citations into it; #1,817 of 1,956 by trust here, #1,287 by citations
• Signal Processing — 71 citations into it
• Multimodal Learning — 62 citations into it
• Evaluation & Benchmarks — 60 citations into it
• Model Architectures — 44 citations into it

Whom Baoyuan Wu trusts overall

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

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

Who trusts Baoyuan Wu overall

1. Zechun Liu
2. Weiwei Liu
3. Yanbo Fan
4. Zhiqiang Shen
5. Weihao Xia
6. Terrance Devries
7. Haotong Qin
8. Naveed Akhtar
9. Andy Zeng
10. Haobo Wang

Papers from Baoyuan Wu's perspective

Ordered by how strongly the researchers Baoyuan Wu trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• TediGAN: Text-Guided Diverse Face Image Generation and Manipulation (2021) — 465 citations
• Learning to Compose Dynamic Tree Structures for Visual Contexts (2018) — 612 citations
• Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning (2019) — 95 citations
• Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm (2018) — 648 citations
• Exact Adversarial Attack to Image Captioning via Structured Output Learning With Latent Variables (2019) — 61 citations
• Sparse Adversarial Attack via Perturbation Factorization (2020) — 98 citations
• Target-Aware Deep Tracking (2019) — 378 citations
• TediGAN: Text-Guided Diverse Image Generation and Manipulation (2020) — 24 citations
• Efficient Decision-Based Black-Box Adversarial Attacks on Face Recognition (2019) — 470 citations
• The Seventh Visual Object Tracking VOT2019 Challenge Results (2019) — 486 citations
• Backdoor Learning: A Survey (2020) — 841 citations
• UCF: Uncovering Common Features for Generalizable Deepfake Detection (2023) — 302 citations
• Towards Open-World Text-Guided Face Image Generation and Manipulation (2021) — 49 citations
• Invisible Backdoor Attack with Sample-Specific Triggers (2020) — 680 citations
• Boosting Decision-Based Black-Box Adversarial Attacks with Random Sign Flip (2020) — 78 citations
• Multi-label Learning with Missing Labels Using Mixed Dependency Graphs (2018) — 91 citations