Changsheng Xu

Computer Vision and Pattern Recognition · 2011–2023

A researcher in the Science corpus: 21 papers; trusts 1,487 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 625 citations into it; #7,625 of 21,575 by trust here, #2,102 by citations
• Artificial Intelligence — 238 citations into it; #30,363 of 35,345 by trust here, #21,878 by citations
• Model Architectures — 74 citations into it
• Generative Models — 69 citations into it
• Representation & Self-Supervised Learning — 69 citations into it; #2,753 of 3,145 by trust here, #3,047 by citations
• Multimodal Learning — 63 citations into it; #763 of 1,211 by trust here, #1,056 by citations
• Evaluation & Benchmarks — 38 citations into it
• Media Technology — 38 citations into it

Whom Changsheng Xu trusts overall

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

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

Who trusts Changsheng Xu overall

1. Xingping Dong
2. Yuxin Peng
3. Seyed Mojtaba Marvasti-Zadeh
4. Dima Damen
5. Ilchae Jung
6. Ziyuan Huang
7. Goutam Bhat
8. Xin Li
9. Kenan Dai
10. Feng Li

Papers from Changsheng Xu's perspective

Ordered by how strongly the researchers Changsheng Xu 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. 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

• The Visual Object Tracking VOT2015 Challenge Results (2015) — 901 citations
• The Visual Object Tracking VOT2016 Challenge Results (2016) — 773 citations
• Street-to-shop: Cross-scenario clothing retrieval via parts alignment and auxiliary set (2012) — 391 citations
• Hi, magic closet, tell me what to wear! (2012) — 287 citations
• Multi-task Correlation Particle Filter for Robust Object Tracking (2017) — 403 citations
• DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis (2020) — 308 citations
• Deep Representation Learning With Part Loss for Person Re-Identification (2017) — 433 citations
• The Visual Object Tracking VOT2017 Challenge Results (2017) — 301 citations
• The Sixth Visual Object Tracking VOT2018 Challenge Results (2018) — 797 citations
• Learning Consistent Feature Representation for Cross-Modal Multimedia Retrieval (2015) — 213 citations
• A Unified Framework for Multimodal Domain Adaptation (2018) — 69 citations
• Hierarchical Multi-modal Contextual Attention Network for Fake News Detection (2021) — 298 citations
• Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer (2021) — 301 citations
• Visual-Language Prompt Tuning with Knowledge-Guided Context Optimization (2023) — 466 citations
• Inversion-based Style Transfer with Diffusion Models (2022) — 478 citations
• StyTr2: Image Style Transfer with Transformers (2021) — 433 citations
• Dynamic Refinement Network for Oriented and Densely Packed Object Detection (2020) — 331 citations
• Explainable Interaction-driven User Modeling over Knowledge Graph for Sequential Recommendation (2019) — 117 citations
• Selective clustering for representative paintings selection (2019) — 9 citations
• Latent feature learning in social media network (2013) — 33 citations