Xiangxiang Chu
Computer Vision and Pattern Recognition · 2019–2023
A researcher in the Science corpus: 12 papers; trusts 1,032 researchers and is trusted by 32 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 393 citations into it; #5,416 of 21,575 by trust here, #2,888 by citations
- Artificial Intelligence — 311 citations into it; #19,642 of 35,345 by trust here, #8,077 by citations
- Model Architectures — 139 citations into it
- Neural Architecture Search & AutoML — 62 citations into it; #99 of 283 by trust here, #91 by citations
- Optimization & Training — 61 citations into it
- Efficiency & Compression — 56 citations into it
- Evaluation & Benchmarks — 43 citations into it
- Media Technology — 30 citations into it
Whom Xiangxiang Chu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Quoc V. Le
- Geoffrey E. Hinton
- Barret Zoph
- Ross B. Girshick
- Yoshua Bengio
- Ilya Sutskever
- Karen Simonyan
- Diederik P. Kingma
Who trusts Xiangxiang Chu overall
- Bo Zhang
- Changlin Li
- Yu-Huan Wu
- Yong Guo
- Shan You
- Zhisheng Lu
- Houwen Peng
- Weijian Xu
- Zilong Huang
- Wenhai Wang
Papers from Xiangxiang Chu's perspective
Ordered by how strongly the researchers Xiangxiang Chu 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
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Twins: Revisiting the Design of Spatial Attention in Vision Transformers (2021) — 1,341 citations
- Do We Really Need Explicit Position Encodings for Vision Transformers? (2021) — 107 citations
- Conditional Positional Encodings for Vision Transformers (2021) — 838 citations
- FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search (2019) — 370 citations
- Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search (2019) — 287 citations
- YOLOv6 v3.0: A Full-Scale Reloading (2023) — 373 citations
- YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022) — 3,214 citations
- DARTS-: Robustly Stepping out of Performance Collapse Without Indicators (2020) — 194 citations
- Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search (2019) — 359 citations
- Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search (2019) — 118 citations
- ScarletNAS: Bridging the Gap Between Scalability and Fairness in Neural Architecture Search (2019) — 27 citations
- SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture Search (2019) — 27 citations