Chunjing Xu
Computer Vision and Pattern Recognition · 2019–2022
A researcher in the Science corpus: 22 papers; trusts 1,783 researchers and is trusted by 33 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 962 citations into it; #4,767 of 21,575 by trust here, #1,461 by citations
- Artificial Intelligence — 617 citations into it; #17,667 of 35,345 by trust here, #5,720 by citations
- Model Architectures — 217 citations into it; #1,546 of 2,278 by trust here, #686 by citations
- Efficiency & Compression — 216 citations into it
- Representation & Self-Supervised Learning — 91 citations into it
- Media Technology — 89 citations into it
- Optimization & Training — 79 citations into it
- Evaluation & Benchmarks — 67 citations into it
Whom Chunjing Xu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Geoffrey E. Hinton
- Ross B. Girshick
- Yoshua Bengio
- Yann LeCun
- Karen Simonyan
- Diederik P. Kingma
- Ilya Sutskever
- Shaoqing Ren
Who trusts Chunjing Xu overall
Papers from Chunjing Xu's perspective
Ordered by how strongly the researchers Chunjing Xu trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- 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
- 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
- Going deeper with convolutions
Their papers
- Transformer in Transformer (2021) — 2,223 citations
- FILIP: Fine-grained Interactive Language-Image Pre-Training (2021) — 865 citations
- Pre-Trained Image Processing Transformer (2020) — 2,255 citations
- GhostNet: More Features From Cheap Operations (2019) — 4,183 citations
- CMT: Convolutional Neural Networks Meet Vision Transformers (2021) — 951 citations
- One Million Scenes for Autonomous Driving: ONCE Dataset (2021) — 383 citations
- Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets (2020) — 111 citations
- DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection (2022) — 265 citations
- Co-Evolutionary Compression for Unpaired Image Translation (2019) — 93 citations
- CARS: Continuous Evolution for Efficient Neural Architecture Search (2019) — 269 citations
- A Survey on Vision Transformer (2020) — 3,751 citations
- AdderNet: Do We Really Need Multiplications in Deep Learning? (2019) — 236 citations
- Learning Instance-wise Sparsity for Accelerating Deep Models (2019) — 30 citations
- Distilling Object Detectors via Decoupled Features (2021) — 258 citations
- Voxel Transformer for 3D Object Detection (2021) — 556 citations
- SODA10M: Towards Large-Scale Object Detection Benchmark for Autonomous Driving (2021) — 49 citations
- Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks (2020) — 41 citations
- Frequency Domain Compact 3D Convolutional Neural Networks (2020) — 32 citations
- Distilling portable Generative Adversarial Networks for Image Translation (2020) — 89 citations
- Efficient Residual Dense Block Search for Image Super-Resolution (2019) — 147 citations