Gang Zeng

Computer Vision and Pattern Recognition · 2006–2024

A researcher in the Science corpus: 14 papers; trusts 1,274 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 446 citations into it; #8,251 of 21,575 by trust here, #3,378 by citations
• Artificial Intelligence — 238 citations into it; #19,703 of 35,345 by trust here, #7,797 by citations
• Computer Graphics and Computer-Aided Design — 79 citations into it
• Model Architectures — 78 citations into it; #1,772 of 2,278 by trust here, #1,509 by citations
• Representation & Self-Supervised Learning — 68 citations into it
• Computational Mechanics — 52 citations into it
• Optimization & Training — 39 citations into it
• Multimodal Learning — 37 citations into it

Whom Gang Zeng trusts overall

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

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

Who trusts Gang Zeng overall

1. Dongyoon Han
2. Feng Li
3. Lihe Yang
4. Ke Zhang
5. Hao Zhang
6. Paul C. Merrell
7. Ruben Michaël Smelik

Papers from Gang Zeng's perspective

Ordered by how strongly the researchers Gang Zeng 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
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

• VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks (2023) — 699 citations
• Conditional DETR for Fast Training Convergence (2021) — 1,038 citations
• Context Autoencoder for Self-supervised Representation Learning (2022) — 508 citations
• DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation (2023) — 1,030 citations
• Image-based procedural modeling of facades (2007) — 603 citations
• Neural Style Transfer via Meta Networks (2018) — 134 citations
• Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision (2021) — 1,179 citations
• Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment (2022) — 254 citations
• LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation (2024) — 817 citations
• Weighted residuals for very deep networks (2016) — 53 citations
• Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation (2020) — 449 citations
• Fast Semantic Image Segmentation with High Order Context and Guided Filtering (2016) — 10 citations
• Fast Neighborhood Graph Search Using Cartesian Concatenation (2013) — 39 citations
• Image-based plant modeling (2006) — 326 citations