Yezhou Yang

4.5k total citations · 1 hit paper
89 papers, 2.1k citations indexed

About

Yezhou Yang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Control and Systems Engineering. According to data from OpenAlex, Yezhou Yang has authored 89 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 65 papers in Computer Vision and Pattern Recognition, 39 papers in Artificial Intelligence and 15 papers in Control and Systems Engineering. Recurrent topics in Yezhou Yang's work include Multimodal Machine Learning Applications (34 papers), Human Pose and Action Recognition (23 papers) and Advanced Image and Video Retrieval Techniques (19 papers). Yezhou Yang is often cited by papers focused on Multimodal Machine Learning Applications (34 papers), Human Pose and Action Recognition (23 papers) and Advanced Image and Video Retrieval Techniques (19 papers). Yezhou Yang collaborates with scholars based in United States, China and Australia. Yezhou Yang's co-authors include Yiannis Aloimonos, Mingli Song, Cornelia Fermüller, Zunlei Feng, Yongcheng Jing, Yizhou Yu, Jingwen Ye, Ching L. Teo, Hal Daumé and Chitta Baral and has published in prestigious journals such as Information Sciences, International Journal of Computer Vision and The International Journal of Robotics Research.

In The Last Decade

Yezhou Yang

81 papers receiving 2.1k citations

Hit Papers

Neural Style Transfer: A Review 2019 2026 2021 2023 2019 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Neural Style Transfer: A Review
    2019 457 citations Yezhou Yang et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Yezhou Yang United States 23 1.5k 784 303 120 115 89 2.1k
Tao Mei China 27 2.2k 1.4× 846 1.1× 201 0.7× 94 0.8× 52 0.5× 102 2.9k
Bo Dai China 21 1.7k 1.1× 768 1.0× 151 0.5× 128 1.1× 128 1.1× 55 2.2k
Peizhao Zhang United States 14 1.4k 0.9× 825 1.1× 138 0.5× 146 1.2× 51 0.4× 28 1.9k
Joseph J. Lim United States 17 1.4k 0.9× 632 0.8× 162 0.5× 88 0.7× 38 0.3× 34 1.8k
Tao Chen China 26 1.2k 0.8× 860 1.1× 127 0.4× 74 0.6× 52 0.5× 130 2.5k
Xuemiao Xu China 21 1.3k 0.8× 313 0.4× 203 0.7× 55 0.5× 96 0.8× 83 1.9k
Jianming Zhang China 22 1.1k 0.7× 485 0.6× 140 0.5× 52 0.4× 125 1.1× 85 1.8k
Caroline Pantofaru United States 19 1.4k 0.9× 466 0.6× 306 1.0× 208 1.7× 50 0.4× 37 2.2k
Sifei Liu United States 19 2.8k 1.8× 382 0.5× 210 0.7× 104 0.9× 183 1.6× 49 3.2k
Henghui Ding Singapore 29 1.8k 1.1× 922 1.2× 63 0.2× 63 0.5× 79 0.7× 78 2.3k

Countries citing papers authored by Yezhou Yang

Since Specialization
Citations

This map shows the geographic impact of Yezhou Yang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yezhou Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yezhou Yang more than expected).

Fields of papers citing papers by Yezhou Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yezhou Yang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yezhou Yang. The network helps show where Yezhou Yang may publish in the future.

Co-authorship network of co-authors of Yezhou Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Yezhou Yang. A scholar is included among the top collaborators of Yezhou Yang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yezhou Yang. Yezhou Yang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Patel, Maitreya, Tejas Gokhale, Chitta Baral, & Yezhou Yang. (2024). ConceptBed: Evaluating Concept Learning Abilities of Text-to-Image Diffusion Models. Proceedings of the AAAI Conference on Artificial Intelligence. 38(13). 14554–14562. 3 indexed citations
2.
3.
Lü, Duo, et al.. (2024). Evaluating Safety Metrics for Vulnerable Road Users at Urban Traffic Intersections Using High-Density Infrastructure LiDAR System. SAE technical papers on CD-ROM/SAE technical paper series. 1.
4.
Luo, Man, Tejas Gokhale, Neeraj Varshney, Yezhou Yang, & Chitta Baral. (2024). Advances in Multimodal Information Retrieval and Generation. 1 indexed citations
5.
Wei, Hua, et al.. (2024). eTraM: Event-Based Traffic Monitoring Dataset. 22637–22646. 3 indexed citations
6.
7.
Luo, Man, Zhiyuan Fang, Tejas Gokhale, Yezhou Yang, & Chitta Baral. (2023). End-to-end Knowledge Retrieval with Multi-modal Queries. 8573–8589. 5 indexed citations
8.
Gokhale, Tejas, Rushil Anirudh, Jayaraman J. Thiagarajan, et al.. (2023). Improving Diversity with Adversarially Learned Transformations for Domain Generalization. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 434–443. 19 indexed citations
9.
Fang, Zhiyuan, Tejas Gokhale, Pratyay Banerjee, Chitta Baral, & Yezhou Yang. (2020). Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning. 840–860. 38 indexed citations
10.
Wang, Zhe, et al.. (2020). Resisting the Distracting-factors in Pedestrian Detection.. arXiv (Cornell University). 2 indexed citations
11.
Farhadi, Mohammad & Yezhou Yang. (2020). TKD: Temporal Knowledge Distillation for Active Perception. 942–951. 14 indexed citations
12.
Gokhale, Tejas, Pratyay Banerjee, Chitta Baral, & Yezhou Yang. (2020). MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering. 878–892. 78 indexed citations
13.
Wang, Jingke, Yue Wang, Dongkun Zhang, Yezhou Yang, & Rong Xiong. (2020). Learning hierarchical behavior and motion planning for autonomous driving. 2235–2242. 30 indexed citations
14.
Ye, Xin, et al.. (2019). GAPLE: Generalizable Approaching Policy LEarning for Robotic Object Searching in Indoor Environment. IEEE Robotics and Automation Letters. 4(4). 4003–4010. 13 indexed citations
15.
Wang, Yiwei, Xin Ye, Yezhou Yang, & Wenlong Zhang. (2017). Collision-free trajectory planning in human-robot interaction through hand movement prediction from vision. 305–310. 26 indexed citations
16.
Ye, Chengxi, Chen Zhao, Yezhou Yang, Cornelia Fermüller, & Yiannis Aloimonos. (2016). LightNet. 1156–1159. 13 indexed citations
17.
Yang, Yezhou, et al.. (2015). Visual Commonsense for Scene Understanding Using Perception, Semantic Parsing and Reasoning.. National Conference on Artificial Intelligence. 9–16. 6 indexed citations
18.
Yang, Yezhou, et al.. (2015). Learning the spatial semantics of manipulation actions through preposition grounding. 5302. 1389–1396. 34 indexed citations
19.
Teo, Ching L., Yezhou Yang, Hal Daumé, Cornelia Fermüller, & Yiannis Aloimonos. (2011). A corpus-guided framework for robotic visual perception. National Conference on Artificial Intelligence. 36–42. 6 indexed citations
20.
Yang, Yezhou, Ching L. Teo, Hal Daumé, & Yiannis Aloimonos. (2011). Corpus-Guided Sentence Generation of Natural Images. Empirical Methods in Natural Language Processing. 444–454. 220 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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