Kongming Liang

1.3k total citations
36 papers, 378 citations indexed

About

Kongming Liang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Epidemiology. According to data from OpenAlex, Kongming Liang has authored 36 papers receiving a total of 378 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Computer Vision and Pattern Recognition, 20 papers in Artificial Intelligence and 5 papers in Epidemiology. Recurrent topics in Kongming Liang's work include Domain Adaptation and Few-Shot Learning (16 papers), Multimodal Machine Learning Applications (13 papers) and Advanced Image and Video Retrieval Techniques (10 papers). Kongming Liang is often cited by papers focused on Domain Adaptation and Few-Shot Learning (16 papers), Multimodal Machine Learning Applications (13 papers) and Advanced Image and Video Retrieval Techniques (10 papers). Kongming Liang collaborates with scholars based in China, United Kingdom and United States. Kongming Liang's co-authors include Zhanyu Ma, Xilin Chen, Hong Chang, Jun Guo, Yuhong Guo, Chengwei Pan, Yizhou Yu, Shiguang Shan, Ruoyi Du and Dongliang Chang and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Image Processing.

In The Last Decade

Kongming Liang

30 papers receiving 373 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kongming Liang China 10 203 169 92 41 35 36 378
Yanda Meng United Kingdom 11 230 1.1× 252 1.5× 227 2.5× 46 1.1× 40 1.1× 28 504
Yin Dai China 9 214 1.1× 162 1.0× 156 1.7× 62 1.5× 20 0.6× 14 455
Shakhnoza Muksimova South Korea 12 128 0.6× 86 0.5× 56 0.6× 22 0.5× 26 0.7× 32 311
Junlong Cheng China 8 237 1.2× 165 1.0× 128 1.4× 80 2.0× 35 1.0× 22 413
Hongchun Lu China 8 169 0.8× 123 0.7× 98 1.1× 38 0.9× 21 0.6× 13 307
Jie-Neng Chen United States 3 263 1.3× 172 1.0× 90 1.0× 31 0.8× 11 0.3× 4 426
Hung N. Pham Vietnam 8 57 0.3× 145 0.9× 120 1.3× 29 0.7× 22 0.6× 19 365
Christoph Baur Germany 5 143 0.7× 322 1.9× 197 2.1× 36 0.9× 20 0.6× 8 458
Haoyu Dong United States 4 139 0.7× 100 0.6× 128 1.4× 51 1.2× 9 0.3× 10 361
Yeqi Bai Singapore 6 114 0.6× 131 0.8× 102 1.1× 45 1.1× 8 0.2× 6 321

Countries citing papers authored by Kongming Liang

Since Specialization
Citations

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

Fields of papers citing papers by Kongming Liang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kongming Liang

This figure shows the co-authorship network connecting the top 25 collaborators of Kongming Liang. A scholar is included among the top collaborators of Kongming Liang 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 Kongming Liang. Kongming Liang 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.
Liang, Kongming, et al.. (2025). Animal-CLIP: A Dual-Prompt Enhanced Vision-Language Model for Animal Action Recognition. International Journal of Computer Vision. 133(8). 5062–5082.
2.
Chang, Dongliang, et al.. (2025). Reserve to Adapt: Mining Inter-Class Relations for Open-Set Domain Adaptation. IEEE Transactions on Image Processing. 34. 1382–1397.
3.
Liang, Kongming, et al.. (2024). Dual-Prior Augmented Decoding Network for Long Tail Distribution in HOI Detection. Proceedings of the AAAI Conference on Artificial Intelligence. 38(3). 1806–1814. 2 indexed citations
4.
Li, Wenjie, et al.. (2024). Efficient Face Super-Resolution via Wavelet-based Feature Enhancement Network. 4515–4523. 14 indexed citations
5.
Liang, Kongming, et al.. (2024). Disentangling Before Composing: Learning Invariant Disentangled Features for Compositional Zero-Shot Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(2). 1132–1147. 3 indexed citations
6.
Du, Ruoyi, Aneeshan Sain, Kongming Liang, et al.. (2024). Understanding Episode Hardness in Few-Shot Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(1). 616–633.
7.
Liang, Kongming, et al.. (2024). Benchmarking Segmentation Models with Mask-Preserved Attribute Editing. 22509–22519.
8.
Zhang, Tian, Kongming Liang, Ke Zhang, & Zhanyu Ma. (2024). Learning Conditional Prompt for Compositional Zero-Shot Learning. 1–6.
9.
Du, Ruoyi, Dongliang Chang, Zhanyu Ma, et al.. (2023). Semi-Supervised Learning for FGVC With Out-of-Category Data. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(5). 2658–2671. 3 indexed citations
10.
Wang, Chunyu, et al.. (2023). Category-Specific Prompts for Animal Action Recognition with Pretrained Vision-Language Models. 5716–5724. 1 indexed citations
11.
Liang, Kongming, et al.. (2023). Hierarchical Visual Attribute Learning in the Wild. 3415–3423. 1 indexed citations
12.
Liang, Kongming, et al.. (2023). Learning Dynamic Prototypes for Visual Pattern Debiasing. International Journal of Computer Vision. 132(5). 1777–1799. 2 indexed citations
13.
Yang, Yuqi, Jiyang Xie, Zhongwei Si, et al.. (2023). Multi-Head Uncertainty Inference for Adversarial Attack Detection. 1–5. 1 indexed citations
14.
15.
Zhao, Gangming, Kongming Liang, Chengwei Pan, et al.. (2022). Graph Convolution Based Cross-Network Multiscale Feature Fusion for Deep Vessel Segmentation. IEEE Transactions on Medical Imaging. 42(1). 183–195. 32 indexed citations
16.
Xia, Xiaona, Qingguo Ren, Jiufa Cui, et al.. (2022). Radiomics for predicting revised hematoma expansion with the inclusion of intraventricular hemorrhage growth in patients with supratentorial spontaneous intraparenchymal hematomas. Annals of Translational Medicine. 10(1). 8–8. 12 indexed citations
17.
Xia, Xiaona, Xiaoqian Zhang, Qingguo Ren, et al.. (2021). Automated detection of 3D midline shift in spontaneous supratentorial intracerebral haemorrhage with non-contrast computed tomography using deep convolutional neural networks.. PubMed Central. 13(10). 11513–11521. 4 indexed citations
18.
Liang, Kongming, Hong Chang, Shiguang Shan, & Xilin Chen. (2019). Visual concept conjunction learning with recurrent neural networks. Neurocomputing. 395. 229–236. 2 indexed citations
19.
Liang, Kongming, Hong Chang, Bingpeng Ma, Shiguang Shan, & Xilin Chen. (2018). Unifying Visual Attribute Learning with Object Recognition in a Multiplicative Framework. IEEE Transactions on Pattern Analysis and Machine Intelligence. 41(7). 1747–1760. 15 indexed citations
20.
Liang, Kongming, Yuhong Guo, Hong Chang, & Xilin Chen. (2017). Incomplete Attribute Learning with auxiliary labels. 2252–2258. 4 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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