Ehsan Abbasnejad

1.7k total citations
41 papers, 894 citations indexed

About

Ehsan Abbasnejad is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Management Science and Operations Research. According to data from OpenAlex, Ehsan Abbasnejad has authored 41 papers receiving a total of 894 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Artificial Intelligence, 18 papers in Computer Vision and Pattern Recognition and 4 papers in Management Science and Operations Research. Recurrent topics in Ehsan Abbasnejad's work include Domain Adaptation and Few-Shot Learning (13 papers), Multimodal Machine Learning Applications (12 papers) and Adversarial Robustness in Machine Learning (6 papers). Ehsan Abbasnejad is often cited by papers focused on Domain Adaptation and Few-Shot Learning (13 papers), Multimodal Machine Learning Applications (12 papers) and Adversarial Robustness in Machine Learning (6 papers). Ehsan Abbasnejad collaborates with scholars based in Australia, Switzerland and Germany. Ehsan Abbasnejad's co-authors include Damith C. Ranasinghe, Bao Gia Doan, Qinfeng Shi, Anton van den Hengel, Damien Teney, Davide Astiaso Garcia, Meysam Majidi Nezhad, Mehdi Neshat, Bradley Alexander and Lina Bertling Tjernberg and has published in prestigious journals such as Energy Conversion and Management, Energy and Sensors.

In The Last Decade

Ehsan Abbasnejad

35 papers receiving 869 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ehsan Abbasnejad Australia	14	534	282	261	97	93	41	894
Mei Yu China	13	382 0.7×	182 0.6×	351 1.3×	94 1.0×	61 0.7×	72	875
Lauro Snidaro Italy	17	437 0.8×	556 2.0×	110 0.4×	129 1.3×	160 1.7×	72	1.1k
Prachi Agrawal India	12	529 1.0×	220 0.8×	99 0.4×	33 0.3×	53 0.6×	39	967
Ahmad Nickabadi Iran	9	503 0.9×	191 0.7×	128 0.5×	43 0.4×	70 0.8×	31	906
Mohammad Taghi Manzuri Iran	15	185 0.3×	324 1.1×	183 0.7×	39 0.4×	94 1.0×	103	778
Yingchi Mao China	19	268 0.5×	451 1.6×	244 0.9×	53 0.5×	389 4.2×	143	1.2k
Juan Zhao China	19	636 1.2×	206 0.7×	144 0.6×	84 0.9×	168 1.8×	106	1.1k

Countries citing papers authored by Ehsan Abbasnejad

Since Specialization

Citations

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

Fields of papers citing papers by Ehsan Abbasnejad

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ehsan Abbasnejad

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

All Works

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20 of 20 papers shown

Mohammadi, Bahram, Ehsan Abbasnejad, Yuankai Qi, et al.. (2025). Parameter-efficient action planning with large language models for vision-and-language navigation. Pattern Recognition. 172. 112462–112462.

Yang, Shuiqiao, Bao Gia Doan, Paul Montague, et al.. (2025). Graph spectral purification for backdoor defence in graph neural networks. World Wide Web. 28(4).

Teney, Damien, et al.. (2024). Neural Redshift: Random Networks are not Random Functions. 4786–4796. 5 indexed citations

Doan, Bao Gia, Shuiqiao Yang, Paul Montague, et al.. (2023). Feature-Space Bayesian Adversarial Learning Improved Malware Detector Robustness. Proceedings of the AAAI Conference on Artificial Intelligence. 37(12). 14783–14791. 4 indexed citations

Booth, David T., et al.. (2023). Deep Metric Learning for Scalable Gait-Based Person Re-Identification Using Force Platform Data. Sensors. 23(7). 3392–3392. 5 indexed citations

Hayat, Munawar, et al.. (2023). LAVA:Label-efficient Visual Learning and Adaptation. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 32. 147–156. 2 indexed citations

Nassar, Islam A., Munawar Hayat, Ehsan Abbasnejad, Hamid Rezatofighi, & Gholamreza Haffari. (2023). Protocon: Pseudo-Label Refinement via Online Clustering and Prototypical Consistency for Efficient Semi-Supervised Learning. 11641–11650. 14 indexed citations

Liu, Lingqiao, et al.. (2022). Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model. 178–186. 7 indexed citations

Abbasnejad, Ehsan, et al.. (2022). Active Learning by Feature Mixing. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 12227–12236. 65 indexed citations

10.

Haghighi, Manouchehr, et al.. (2021). A data-driven reservoir simulation for natural gas reservoirs. Neural Computing and Applications. 33(18). 11777–11798. 12 indexed citations

11.

Wu, Qi, et al.. (2021). Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 10 indexed citations

12.

Neshat, Mehdi, Meysam Majidi Nezhad, Ehsan Abbasnejad, et al.. (2021). A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm. Energy Conversion and Management. 236. 114002–114002. 178 indexed citations

13.

Teney, Damien, Ehsan Abbasnejad, Kushal Kafle, et al.. (2020). On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 33. 407–417. 7 indexed citations

14.

Abbasnejad, Ehsan, et al.. (2020). Counterfactual Vision-and-Language Navigation: Unravelling the Unseen. Neural Information Processing Systems. 33. 5296–5307. 13 indexed citations

15.

Doan, Bao Gia, Ehsan Abbasnejad, & Damith C. Ranasinghe. (2020). Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems. Annual Computer Security Applications Conference. 897–912. 141 indexed citations

16.

Doan, Bao Gia, Ehsan Abbasnejad, & Damith C. Ranasinghe. (2019). DeepCleanse: A Black-box Input SanitizationFramework Against Backdoor Attacks on DeepNeural Networks. arXiv (Cornell University). 1 indexed citations

17.

Abbasnejad, Ehsan, et al.. (2018). 3D Hand Pose Estimation using Simulation and Partial-Supervision with a Shared Latent Space. Own your potential (DEAKIN). 1–16.

18.

Ismail, Ahmad Izani Md., et al.. (2013). A note on solving the fuzzy Sylvester matrix equation. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 3 indexed citations

19.

Abbasnejad, Ehsan, Scott Sanner, Edwin V. Bonilla, & Pascal Poupart. (2013). Learning community-based preferences via dirichlet process mixtures of Gaussian processes. ANU Open Research (Australian National University). 1213–1219. 13 indexed citations

20.

Abbasbandy, S., et al.. (2011). The common solution of the pair of fuzzy matrix equations. World Applied Sciences Journal. 3 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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