Peng Gao

Computer Vision and Pattern Recognition · International Conference on Learning Representations · 2018–2024

A researcher in the Science corpus: 25 papers; trusts 2,046 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 1,107 citations into it; #6,369 of 21,575 by trust here, #1,567 by citations
• Artificial Intelligence — 937 citations into it; #11,896 of 35,345 by trust here, #2,070 by citations
• Multimodal Learning — 325 citations into it; #566 of 1,211 by trust here, #199 by citations
• Large Language Models — 213 citations into it
• Model Architectures — 193 citations into it
• Evaluation & Benchmarks — 163 citations into it
• Representation & Self-Supervised Learning — 151 citations into it
• Natural Language Processing — 141 citations into it

Whom Peng Gao trusts overall

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

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

Who trusts Peng Gao overall

1. Renrui Zhang
2. Pan Lu
3. Ziyu Guo
4. Shijie Geng
5. Hongsheng Li
6. Jiaming Han
7. Xinzhe Han
8. Shilong Liu
9. Zeyu Han
10. Dalu Guo

Papers from Peng Gao's perspective

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

• CLIP-Adapter: Better Vision-Language Models with Feature Adapters (2021) — 1,792 citations
• LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention (2023) — 1,040 citations
• Dynamic Fusion With Intra- and Inter-Modality Attention Flow for Visual Question Answering (2018) — 416 citations
• Fast Convergence of DETR with Spatially Modulated Co-Attention (2021) — 404 citations
• LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model (2023) — 758 citations
• Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling (2021) — 591 citations
• Question-Guided Hybrid Convolution for Visual Question Answering (2018) — 71 citations
• ImageBind-LLM: Multi-modality Instruction Tuning (2023) — 181 citations
• Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training (2022) — 417 citations
• LVLM-EHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models (2023) — 280 citations
• UniFormer: Unifying Convolution and Self-Attention for Visual Recognition (2022) — 610 citations
• UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning (2022) — 368 citations
• Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classification (2022) — 616 citations
• Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following (2023) — 235 citations
• SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models (2024) — 159 citations
• Multi-Modality Latent Interaction Network for Visual Question Answering (2019) — 87 citations
• RomeBERT: Robust Training of Multi-Exit BERT (2021) — 32 citations
• MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems? (2024) — 698 citations
• SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models (2023) — 306 citations
• Personalize Segment Anything Model with One Shot (2023) — 372 citations