Wenqi Shao
Computer Vision and Pattern Recognition · with Zhang · 2018–2023
A researcher in the Science corpus: 7 papers; trusts 498 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 314 citations into it
- Artificial Intelligence — 289 citations into it; #19,844 of 35,345 by trust here, #8,157 by citations
- Evaluation & Benchmarks — 70 citations into it
- Optimization & Training — 37 citations into it; #2,100 of 2,601 by trust here, #1,766 by citations
- Model Architectures — 35 citations into it
- Efficiency & Compression — 27 citations into it
- Representation & Self-Supervised Learning — 19 citations into it
- Scaling & Learning Theory — 12 citations into it
Whom Wenqi Shao trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Geoffrey E. Hinton
- Ross B. Girshick
- Diederik P. Kingma
- Yoshua Bengio
- Ilya Sutskever
- Yann LeCun
- Christian Szegedy
- Sergey Ioffe
Who trusts Wenqi Shao overall
Papers from Wenqi Shao's perspective
Ordered by how strongly the researchers Wenqi Shao 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)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest (2023) — 363 citations
- Towards Understanding Regularization in Batch Normalization (2018) — 195 citations
- ImageBind-LLM: Multi-modality Instruction Tuning (2023) — 181 citations
- LVLM-EHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models (2023) — 280 citations
- SSN: Learning Sparse Switchable Normalization via SparsestMax (2019) — 57 citations
- SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models (2023) — 306 citations
- OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models (2023) — 460 citations