Liangjun Zang

758 total citations
17 papers, 173 citations indexed

About

Liangjun Zang is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Liangjun Zang has authored 17 papers receiving a total of 173 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 6 papers in Information Systems and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Liangjun Zang's work include Topic Modeling (9 papers), Natural Language Processing Techniques (4 papers) and Generative Adversarial Networks and Image Synthesis (3 papers). Liangjun Zang is often cited by papers focused on Topic Modeling (9 papers), Natural Language Processing Techniques (4 papers) and Generative Adversarial Networks and Image Synthesis (3 papers). Liangjun Zang collaborates with scholars based in China, Australia and United States. Liangjun Zang's co-authors include Songlin Hu, Jizhong Han, Xing Wu, Longtao Huang, Shuai Zhang, Mingming Li, Fuqing Zhu, Cungen Cao, Rong Zhang and Xiaohui Song and has published in prestigious journals such as World Wide Web, Journal of Computer Science and Technology and Proceedings of the AAAI Conference on Artificial Intelligence.

In The Last Decade

Liangjun Zang

14 papers receiving 166 citations

Peers

Liangjun Zang

AI

IS

CVPR

ECP

MSOR

Quoc-Tuan Truong Singapore

Ryohei Sasano Japan

Kaichun Yao China

Rumei Li China

Wuwei Lan United States

Yassine Benajiba United States

Chong Teng China

Haozhe Ji China

Diganta Saha India

Yitao Cai China

Quoc-Tuan Truong Singapore

Liangjun Zang

185 ×1.4

AI

105 ×2.2

IS

83 ×2.2

CVPR

21 ×1.0

ECP

12 ×1.0

MSOR

Citations per year, relative to Liangjun Zang Liangjun Zang (= 1×) peers Quoc-Tuan Truong

Countries citing papers authored by Liangjun Zang

Since Specialization

Citations

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

Fields of papers citing papers by Liangjun Zang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Liangjun Zang

This figure shows the co-authorship network connecting the top 25 collaborators of Liangjun Zang. A scholar is included among the top collaborators of Liangjun Zang 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 Liangjun Zang. Liangjun Zang 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:

17 of 17 papers shown

1.

Cai, Yu, et al.. (2024). HIDD: Human-perception-centric Incremental Deepfake Detection. 1–6. 1 indexed citations

2.

Li, Zhaoxing, et al.. (2024). Explainable Deepfake Detection with Human Prompts. 3023–3029.

3.

Zang, Liangjun, et al.. (2024). Triple-Based Data Augmentation for Event Temporal Extraction via Reinforcement Learning from Human Feedback. 1627–1632.

4.

Dai, Jiao, et al.. (2024). HDDA: Human-perception-centric Deepfake Detection Adapter. 1–9. 1 indexed citations

5.

Zhang, Xiaobin, et al.. (2024). Event Temporal Relation Extraction based on Retrieval-Augmented on LLMs. 1–8.

6.

Lü, Qianqian, et al.. (2024). Adaptive Mixture of Domain-aware Experts for Detecting Social Bots. 2072–2077. 1 indexed citations

7.

Wu, Xing, et al.. (2022). Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks. 871–875. 18 indexed citations

8.

Song, Xiaohui, Liangjun Zang, Rong Zhang, Songlin Hu, & Longtao Huang. (2022). Emotionflow: Capture the Dialogue Level Emotion Transitions. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 8542–8546. 25 indexed citations

9.

Zang, Liangjun, et al.. (2022). A Knowledge/Data Enhanced Method for Joint Event and Temporal Relation Extraction. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 6362–6366. 2 indexed citations

10.

Lv, Shangwen, Longtao Huang, Liangjun Zang, et al.. (2020). Yet another approach to understanding news event evolution. World Wide Web. 23(4). 2449–2470. 4 indexed citations

11.

Li, Mingming, Shuai Zhang, Fuqing Zhu, et al.. (2020). Symmetric Metric Learning with Adaptive Margin for Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence. 34(4). 4634–4641. 40 indexed citations

12.

Wu, Xing, et al.. (2019). Mask and Infill: Applying Masked Language Model for Sentiment Transfer. 5271–5277. 54 indexed citations

13.

Li, Mingming, Jiao Dai, Fuqing Zhu, et al.. (2019). A Fuzzy Set Based Approach for Rating Bias. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 9969–9970. 1 indexed citations

14.

Huang, Longtao, et al.. (2018). A Fresh Look at Understanding News Events Evolution. 29–30. 2 indexed citations

15.

Zang, Liangjun, Cong Cao, Yanan Cao, Yu-Ming Wu, & Cungen Cao. (2013). A Survey of Commonsense Knowledge Acquisition. Journal of Computer Science and Technology. 28(4). 689–719. 18 indexed citations

16.

Zang, Liangjun, et al.. (2008). A manual experiment on commonsense knowledge acquisition from web corpora. 1564–1569. 4 indexed citations

17.

Cao, Yanan, et al.. (2008). Acquiring Commonsense Knowledge about Properties of Concepts from Text. 4. 155–159. 2 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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