Xinchao Wang

Computer Vision and Pattern Recognition · 2017–2024

A researcher in the Science corpus: 35 papers; trusts 2,646 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 1,141 citations into it; #5,216 of 21,575 by trust here, #1,842 by citations
• Artificial Intelligence — 864 citations into it; #7,102 of 35,345 by trust here, #4,137 by citations
• Model Architectures — 316 citations into it
• Efficiency & Compression — 181 citations into it; #1,218 of 1,264 by trust here, #827 by citations
• Representation & Self-Supervised Learning — 148 citations into it
• Optimization & Training — 119 citations into it
• Generative Models — 109 citations into it
• Evaluation & Benchmarks — 98 citations into it

Whom Xinchao Wang trusts overall

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

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

Who trusts Xinchao Wang overall

1. Lin Wang
2. Jingwen Ye
3. Jianping Gou
4. Dongze Lian
5. Chengchao Shen
6. Kuk-Jin Yoon
7. Yiding Yang
8. Baosheng Yu
9. Sihui Luo
10. Mingli Song

Papers from Xinchao Wang's perspective

Ordered by how strongly the researchers Xinchao Wang 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
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

• NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results (2017) — 1,901 citations
• MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities (2023) — 1,322 citations
• Wide Activation for Efficient and Accurate Image Super-Resolution (2018) — 381 citations
• Balanced Two-Stage Residual Networks for Image Super-Resolution (2017) — 108 citations
• On Compressing Deep Models by Low Rank and Sparse Decomposition (2017) — 482 citations
• How Well Does Self-Supervised Pre-Training Perform with Streaming Data? (2021) — 39 citations
• Horizontal Pyramid Matching for Person Re-identification (2018) — 505 citations
• Distilling Knowledge From Graph Convolutional Networks (2020) — 287 citations
• Learning with Recoverable Forgetting (2022) — 53 citations
• LLM-Pruner: On the Structural Pruning of Large Language Models (2023) — 892 citations
• Factorizable Graph Convolutional Networks (2020) — 172 citations
• MambaOut: Do We Really Need Mamba for Vision?* (2024) — 309 citations
• InceptionNeXt: When Inception Meets ConvNeXt (2023) — 350 citations
• DeepCache: Accelerating Diffusion Models for Free (2023) — 400 citations
• DepGraph: Towards Any Structural Pruning (2023) — 518 citations
• Factorizing Knowledge in Neural Networks (2022) — 136 citations
• Point2Seq: Detecting 3D Objects as Sequences (2022) — 28 citations
• Inception Transformer (2022) — 300 citations
• Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning (2022) — 390 citations
• MetaFormer Baselines for Vision (2022) — 348 citations