Yujuan Ding

925 total citations · 1 hit paper
24 papers, 416 citations indexed

About

Yujuan Ding is a scholar working on Computer Vision and Pattern Recognition, Information Systems and Artificial Intelligence. According to data from OpenAlex, Yujuan Ding has authored 24 papers receiving a total of 416 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computer Vision and Pattern Recognition, 8 papers in Information Systems and 7 papers in Artificial Intelligence. Recurrent topics in Yujuan Ding's work include Generative Adversarial Networks and Image Synthesis (8 papers), Advanced Image and Video Retrieval Techniques (7 papers) and Recommender Systems and Techniques (6 papers). Yujuan Ding is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (8 papers), Advanced Image and Video Retrieval Techniques (7 papers) and Recommender Systems and Techniques (6 papers). Yujuan Ding collaborates with scholars based in Hong Kong, China and Singapore. Yujuan Ding's co-authors include Tat‐Seng Chua, Wai Keung Wong, Qing Li, Wenqi Fan, Liangbo Ning, Hengyun Li, Dawei Yin, Shijie Wang, Yi Bin and Zhihui Lai and has published in prestigious journals such as ACM Computing Surveys, IEEE Transactions on Circuits and Systems for Video Technology and Applied Sciences.

In The Last Decade

Yujuan Ding

23 papers receiving 397 citations

Hit Papers

align trajectories

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.

A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models

2024 137 citations Wenqi Fan, Yujuan Ding et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yujuan Ding Hong Kong	11	193	148	75	31	30	24	416
Yichao Zhou China	13	117 0.6×	310 2.1×	35 0.5×	48 1.5×	15 0.5×	29	571
Anurag Bhardwaj United States	13	452 2.3×	171 1.2×	37 0.5×	32 1.0×	14 0.5×	27	567
Hongfu Liu United States	11	241 1.2×	243 1.6×	26 0.3×	23 0.7×	8 0.3×	26	435
Youngmin Kim South Korea	10	271 1.4×	71 0.5×	23 0.3×	67 2.2×	35 1.2×	46	446
Yingming Li China	15	339 1.8×	264 1.8×	41 0.5×	21 0.7×	12 0.4×	55	557
Sraban Kumar Mohanty India	14	138 0.7×	290 2.0×	82 1.1×	9 0.3×	20 0.7×	33	442
Kenji Kita Japan	10	118 0.6×	310 2.1×	79 1.1×	9 0.3×	12 0.4×	114	494
Hayato Kobayashi Japan	12	105 0.5×	257 1.7×	47 0.6×	56 1.8×	13 0.4×	31	445
Dehong Gao China	12	192 1.0×	321 2.2×	109 1.5×	7 0.2×	12 0.4×	48	516

Countries citing papers authored by Yujuan Ding

Since Specialization

Citations

This map shows the geographic impact of Yujuan Ding'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 Yujuan Ding with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yujuan Ding more than expected).

Fields of papers citing papers by Yujuan Ding

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yujuan Ding. 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 Yujuan Ding. The network helps show where Yujuan Ding may publish in the future.

Co-authorship network of co-authors of Yujuan Ding

This figure shows the co-authorship network connecting the top 25 collaborators of Yujuan Ding. A scholar is included among the top collaborators of Yujuan Ding 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 Yujuan Ding. Yujuan Ding is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Ning, Liangbo, Yujuan Ding, Wenqi Fan, et al.. (2025). A Survey of WebAgents: Towards Next-Generation AI Agents for Web Automation with Large Foundation Models. CityU Scholars. 6140–6150. 3 indexed citations

2.

Wu, Pangjing, et al.. (2025). Towards Retrieval-Augmented Large Language Models: Data Management and System Design. 4509–4512. 1 indexed citations

3.

Fan, Wenqi, Yujuan Ding, Liangbo Ning, et al.. (2024). A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models. 6491–6501. 137 indexed citations breakdown →

4.

Ding, Yujuan, et al.. (2024). FashionReGen: LLM-Empowered Fashion Report Generation. 991–994. 10 indexed citations

5.

Ding, Yujuan, Wenqi Fan, Xiao Huang, & Qing Li. (2024). Large Language Models for Graph Learning. 1643–1646. 2 indexed citations

6.

Liao, S Y, et al.. (2024). Reproducibility Companion Paper: Recommendation of Mix-and-Match Clothing by Modeling Indirect Personal Compatibility. PolyU Institutional Research Archive (Hong Kong Polytechnic University). 1224–1227.

7.

Lai, Zhihui, et al.. (2024). Deep Scaling Factor Quantization Network for Large-scale Image Retrieval. 851–859. 3 indexed citations

8.

Ding, Yujuan, et al.. (2023). Personalized fashion outfit generation with user coordination preference learning. Information Processing & Management. 60(5). 103434–103434. 19 indexed citations

9.

Bin, Yi, Wenhao Shi, Bin Ji, et al.. (2023). Non-Autoregressive Sentence Ordering. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 4198–4214. 1 indexed citations

10.

Liao, S Y, Yujuan Ding, & P.Y. Mok. (2023). Recommendation of Mix-and-Match Clothing by Modeling Indirect Personal Compatibility. 560–564. 4 indexed citations

11.

Ding, Yujuan, et al.. (2022). Modeling Field-Level Factor Interactions for Fashion Recommendation. 2022 IEEE International Conference on Multimedia and Expo (ICME). 1–6. 6 indexed citations

12.

Bin, Yi, Wenhao Shi, Jipeng Zhang, et al.. (2022). Non-Autoregressive Cross-Modal Coherence Modelling. Proceedings of the 30th ACM International Conference on Multimedia. 3253–3261. 7 indexed citations

13.

Bin, Yi, et al.. (2021). Entity Slot Filling for Visual Captioning. IEEE Transactions on Circuits and Systems for Video Technology. 32(1). 52–62. 32 indexed citations

14.

Ding, Yujuan, et al.. (2021). Leveraging Multiple Relations for Fashion Trend Forecasting Based on Social Media. IEEE Transactions on Multimedia. 24. 2287–2299. 18 indexed citations

15.

Bin, Yi, Xindi Shang, Bo Peng, Yujuan Ding, & Tat‐Seng Chua. (2021). Multi-Perspective Video Captioning. 5110–5118. 8 indexed citations

16.

Ding, Yujuan, Yunshan Ma, Wai Keung Wong, & Tat‐Seng Chua. (2021). Modeling Instant User Intent and Content-Level Transition for Sequential Fashion Recommendation. IEEE Transactions on Multimedia. 24. 2687–2700. 19 indexed citations

17.

Ding, Yujuan, et al.. (2020). Knowledge Enhanced Neural Fashion Trend Forecasting. National University of Singapore. 82–90. 35 indexed citations

18.

Ding, Yujuan, Wai Keung Wong, Zhihui Lai, & Yudong Chen. (2019). Study on 2D Feature-Based Hash Learning. IEEE Transactions on Multimedia. 22(5). 1298–1309. 4 indexed citations

19.

Chen, Yudong, et al.. (2019). Deep Supervised Hashing With Anchor Graph. 9795–9803. 44 indexed citations

20.

Ding, Yujuan, Wai Keung Wong, Zhihui Lai, & Zheng Zhang. (2019). Bilinear Supervised Hashing Based on 2D Image Features. IEEE Transactions on Circuits and Systems for Video Technology. 30(2). 590–602. 13 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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