Pengda Qin

868 total citations
14 papers, 455 citations indexed

About

Pengda Qin is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Pengda Qin has authored 14 papers receiving a total of 455 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 1 paper in Information Systems. Recurrent topics in Pengda Qin's work include Topic Modeling (9 papers), Natural Language Processing Techniques (7 papers) and Advanced Image and Video Retrieval Techniques (3 papers). Pengda Qin is often cited by papers focused on Topic Modeling (9 papers), Natural Language Processing Techniques (7 papers) and Advanced Image and Video Retrieval Techniques (3 papers). Pengda Qin collaborates with scholars based in China, United States and United Kingdom. Pengda Qin's co-authors include William Yang Wang, Weiran Xu, Weiran Xu, Jun Guo, Wenhu Chen, Chunyun Zhang, Xin Wang, Ting Liu, Shaolei Wang and Qi Liu and has published in prestigious journals such as Neurocomputing, Journal of Control Science and Engineering and Proceedings of the AAAI Conference on Artificial Intelligence.

In The Last Decade

Pengda Qin

12 papers receiving 435 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pengda Qin China 9 370 94 41 28 17 14 455
Jinan Xu China 12 624 1.7× 126 1.3× 71 1.7× 44 1.6× 19 1.1× 94 703
Zhixing Tan China 8 365 1.0× 118 1.3× 36 0.9× 12 0.4× 15 0.9× 19 454
Wenhan Xiong United States 10 515 1.4× 108 1.1× 58 1.4× 52 1.9× 25 1.5× 22 564
Mengting Hu China 11 212 0.6× 62 0.7× 34 0.8× 12 0.4× 7 0.4× 43 323
Minwei Feng Germany 8 431 1.2× 124 1.3× 101 2.5× 14 0.5× 19 1.1× 13 484
Meng Zhao China 8 318 0.9× 61 0.6× 45 1.1× 10 0.4× 23 1.4× 34 380
Weiqiang Jin China 9 221 0.6× 50 0.5× 44 1.1× 18 0.6× 6 0.4× 25 336
Tengfei Liu China 9 169 0.5× 62 0.7× 49 1.2× 8 0.3× 26 1.5× 37 263
Xinchi Chen China 10 453 1.2× 106 1.1× 38 0.9× 7 0.3× 33 1.9× 12 508
Weihua Luo China 14 462 1.2× 187 2.0× 37 0.9× 10 0.4× 7 0.4× 44 505

Countries citing papers authored by Pengda Qin

Since Specialization
Citations

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

Fields of papers citing papers by Pengda Qin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pengda Qin

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

All Works

14 of 14 papers shown
1.
Qin, Pengda, et al.. (2024). Synthesizing Coherent Story with Auto-Regressive Latent Diffusion Models. 2908–2918. 17 indexed citations
2.
Xu, Peng, Pengda Qin, Deng-Ping Fan, et al.. (2024). LAKE-RED: Camouflaged Images Generation by Latent Background Knowledge Retrieval-Augmented Diffusion. 4092–4101. 6 indexed citations
3.
Deng, Chaorui, Qi Chen, Pengda Qin, Da Chen, & Qi Wu. (2023). Prompt Switch: Efficient CLIP Adaptation for Text-Video Retrieval. 15602–15612. 18 indexed citations
4.
Wang, Xin, et al.. (2023). Decouple Before Interact: Multi-Modal Prompt Learning for Continual Visual Question Answering. 2941–2950. 5 indexed citations
5.
Qin, Pengda, Xin Wang, Wenhu Chen, et al.. (2020). Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs. Proceedings of the AAAI Conference on Artificial Intelligence. 34(5). 8673–8680. 44 indexed citations
6.
Wang, Shaolei, et al.. (2020). Multi-Task Self-Supervised Learning for Disfluency Detection. Proceedings of the AAAI Conference on Artificial Intelligence. 34(5). 9193–9200. 31 indexed citations
7.
Qin, Pengda, Weiran Xu, & William Yang Wang. (2018). Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning. 2137–2147. 140 indexed citations
8.
Qin, Pengda, Weiran Xu, & William Yang Wang. (2018). DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction. 90 indexed citations
9.
Qin, Pengda, Weiran Xu, & Jun Guo. (2017). Providing Definitive Learning Direction for Relation Classification System. Journal of Control Science and Engineering. 2017. 1–10.
10.
Qin, Pengda, Weiran Xu, & Jun Guo. (2017). Designing an adaptive attention mechanism for relation classification. 4356–4362. 18 indexed citations
11.
Qin, Pengda, Weiran Xu, & Jun Guo. (2016). An empirical convolutional neural network approach for semantic relation classification. Neurocomputing. 190. 1–9. 59 indexed citations
12.
Xu, Weiran, et al.. (2016). Fine-grained entity typing for knowledge base completion. 361–365. 3 indexed citations
13.
Qin, Pengda, et al.. (2015). A novel negative sampling based on TFIDF for learning word representation. Neurocomputing. 177. 257–265. 24 indexed citations
14.

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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2026