Renrui Zhang
Computer Vision and Pattern Recognition · 2021–2025
A researcher in the Science corpus: 18 papers; trusts 1,242 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 821 citations into it; #12,552 of 21,575 by trust here, #2,536 by citations
- Artificial Intelligence — 707 citations into it; #20,630 of 35,345 by trust here, #3,323 by citations
- Multimodal Learning — 305 citations into it; #774 of 1,211 by trust here, #309 by citations
- Large Language Models — 196 citations into it
- Evaluation & Benchmarks — 138 citations into it
- Representation & Self-Supervised Learning — 112 citations into it
- Natural Language Processing — 103 citations into it
- Model Architectures — 82 citations into it
Whom Renrui Zhang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Geoffrey E. Hinton
- Diederik P. Kingma
- Ross B. Girshick
- Yoshua Bengio
- Ilya Sutskever
- Alec Radford
- Yann LeCun
- Karen Simonyan
Who trusts Renrui Zhang overall
- Peng Gao
- Jiaming Han
- Ziyu Guo
- Zeyu Han
- Runsen Xu
- Bolin Ni
- Muhammad Uzair Khattak
- Chao Gao
- Bin Lin
- Lvmin Zhang
Papers from Renrui Zhang's perspective
Ordered by how strongly the researchers Renrui Zhang trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet Large Scale Visual Recognition Challenge
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
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
- 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
- ImageBind-LLM: Multi-modality Instruction Tuning (2023) — 181 citations
- Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training (2022) — 417 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
- Seed1.5-VL Technical Report (2025) — 350 citations
- LLaVA-OneVision: Easy Visual Task Transfer (2024) — 2,742 citations
- Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis (2024) — 1,420 citations
- LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models (2024) — 619 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
- Prompt, Generate, Then Cache: Cascade of Foundation Models Makes Strong Few-Shot Learners (2023) — 245 citations
- PointCLIP: Point Cloud Understanding by CLIP (2021) — 671 citations