Xi Li

Computer Vision and Pattern Recognition · IEEE International Conference on Computer Vision · 2011–2023

A researcher in the Science corpus: 26 papers; trusts 2,161 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 873 citations into it; #6,342 of 21,575 by trust here, #2,754 by citations
• Artificial Intelligence — 524 citations into it; #24,133 of 35,345 by trust here, #7,606 by citations
• Generative Models — 132 citations into it
• Model Architectures — 108 citations into it
• Meta & Continual Learning — 107 citations into it
• Representation & Self-Supervised Learning — 97 citations into it
• Optimization & Training — 63 citations into it
• Natural Language Processing — 54 citations into it

Whom Xi Li trusts overall

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

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

Who trusts Xi Li overall

1. Houwen Peng
2. Huchuan Lu
3. Wenguan Wang
4. Tiantian Wang
5. Qibin Hou
6. Ali Borji
7. Pingping Zhang
8. Ming-Ming Cheng
9. Wei David Liu
10. Zhong-Qiu Zhao

Papers from Xi Li's perspective

Ordered by how strongly the researchers Xi Li trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• A survey of appearance models in visual object tracking (2013) — 771 citations
• Deeply-Learned Part-Aligned Representations for Person Re-identification (2017) — 795 citations
• Contextual Hypergraph Modeling for Salient Object Detection (2013) — 199 citations
• When Video Classification Meets Incremental Classes (2021) — 32 citations
• DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection (2015) — 511 citations
• Learning Hash Functions Using Column Generation (2013) — 57 citations
• Graph-Guided Architecture Search for Real-Time Semantic Segmentation (2019) — 108 citations
• Memory-Efficient Class-Incremental Learning for Image Classification (2020) — 108 citations
• Progressive Blockwise Knowledge Distillation for Neural Network Acceleration (2018) — 73 citations
• What and Where: Learn to Plug Adapters via NAS for Multidomain Learning (2020) — 21 citations
• MgSvF: Multi-Grained Slow versus Fast Framework for Few-Shot Class-Incremental Learning (2020) — 151 citations
• Knowledge-Guided Agent-Tactic-Aware Learning for StarCraft Micromanagement (2018) — 14 citations
• LayoutDiffusion: Controllable Diffusion Model for Layout-to-Image Generation (2023) — 352 citations
• Recent Advances and Trends in Multimodal Deep Learning: A Review (2021) — 71 citations
• FcaNet: Frequency Channel Attention Networks (2020) — 1,135 citations
• Few-Shot Class-Incremental Learning via Feature Space Composition (2020) — 6 citations
• Learning Multi-Level Density Maps for Crowd Counting (2020) — 59 citations
• Ultra Fast Structure-aware Deep Lane Detection (2020) — 547 citations
• Deep Attentive Video Summarization With Distribution Consistency Learning (2020) — 83 citations
• A Survey on Generative Adversarial Networks: Variants, Applications, and Training (2020) — 364 citations