Tsung-Wei Ke

466 total citations
12 papers, 101 citations indexed

About

Tsung-Wei Ke is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Tsung-Wei Ke has authored 12 papers receiving a total of 101 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 10 papers in Artificial Intelligence and 1 paper in Molecular Biology. Recurrent topics in Tsung-Wei Ke's work include Advanced Image and Video Retrieval Techniques (8 papers), Domain Adaptation and Few-Shot Learning (7 papers) and Advanced Neural Network Applications (4 papers). Tsung-Wei Ke is often cited by papers focused on Advanced Image and Video Retrieval Techniques (8 papers), Domain Adaptation and Few-Shot Learning (7 papers) and Advanced Neural Network Applications (4 papers). Tsung-Wei Ke collaborates with scholars based in United States, Taiwan and South Korea. Tsung-Wei Ke's co-authors include Stella X. Yu, Jyh-Jing Hwang, Michael Maire, Xudong Wang, Yunhui Guo, Jianbo Shi, Ziwei Liu, Ayush Jain, Katerina Fragkiadaki and Tyng-Luh Liu and has published in prestigious journals such as PLoS ONE, Pattern Recognition Letters and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

In The Last Decade

Tsung-Wei Ke

11 papers receiving 98 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tsung-Wei Ke United States	5	76	42	7	6	6	12	101
Vighnesh Birodkar United States	4	141 1.9×	60 1.4×	3 0.4×	4 0.7×	7 1.2×	4	168
Tianyu Ding United States	4	128 1.7×	22 0.5×	4 0.6×	10 1.7×	3 0.5×	15	164
Ya-Liang Chang Taiwan	5	206 2.7×	35 0.8×	7 1.0×	8 1.3×	7 1.2×	7	233
Jeffrey De Fauw United States	2	63 0.8×	47 1.1×	3 0.4×	9 1.5×	9 1.5×	3	102
Tiziano Portenier Switzerland	6	142 1.9×	21 0.5×	4 0.6×	10 1.7×	9 1.5×	12	161
Samarth Sinha Canada	6	53 0.7×	29 0.7×	3 0.4×	10 1.7×	4 0.7×	11	85
Jungbeom Lee South Korea	4	148 1.9×	96 2.3×	6 0.9×	4 0.7×	21 3.5×	6	209
Manoj Sharma India	8	127 1.7×	15 0.4×	7 1.0×	7 1.2×	5 0.8×	16	161
Mengmeng Xu China	7	117 1.5×	52 1.2×	15 2.1×	11 1.8×	7 1.2×	13	154
Eduardo Pérez-Pellitero Germany	8	196 2.6×	24 0.6×	8 1.1×	17 2.8×	8 1.3×	17	225

Countries citing papers authored by Tsung-Wei Ke

Since Specialization

Citations

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

Fields of papers citing papers by Tsung-Wei Ke

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tsung-Wei Ke. 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 Tsung-Wei Ke. The network helps show where Tsung-Wei Ke may publish in the future.

Co-authorship network of co-authors of Tsung-Wei Ke

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

12 of 12 papers shown

1.

Ke, Tsung-Wei, et al.. (2024). Diffusion-ES: Gradient-Free Planning with Diffusion for Autonomous and Instruction-Guided Driving. 15342–15353. 7 indexed citations

2.

Ke, Tsung-Wei, et al.. (2024). Deep learning workflow to support in-flight processing of digital aerial imagery for wildlife population surveys. PLoS ONE. 19(4). e0288121–e0288121. 2 indexed citations

3.

Kim, Dong-Jin, Tsung-Wei Ke, & Stella X. Yu. (2023). Local pseudo-attributes for long-tailed recognition. Pattern Recognition Letters. 172. 51–57.

4.

Ke, Tsung-Wei, Jyh-Jing Hwang, Yunhui Guo, Xudong Wang, & Stella X. Yu. (2022). Unsupervised Hierarchical Semantic Segmentation with Multiview Cosegmentation and Clustering Transformers. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2561–2571. 28 indexed citations

5.

Ke, Tsung-Wei, Jyh-Jing Hwang, & Stella X. Yu. (2021). Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning. arXiv (Cornell University). 4 indexed citations

6.

Hwang, Jyh-Jing, Tsung-Wei Ke, & Stella X. Yu. (2021). Contextual Image Parsing via Panoptic Segment Sorting. 27–36. 1 indexed citations

7.

Hwang, Jyh-Jing, Tsung-Wei Ke, Jianbo Shi, & Stella X. Yu. (2019). Adversarial Structure Matching for Structured Prediction Tasks. 4051–4060. 11 indexed citations

8.

Ke, Tsung-Wei, Jyh-Jing Hwang, Ziwei Liu, & Stella X. Yu. (2018). Adaptive Affinity Field for Semantic Segmentation.. 10 indexed citations

9.

Ke, Tsung-Wei, Michael Maire, & Stella X. Yu. (2017). Multigrid Neural Architectures. 4067–4075. 32 indexed citations

10.

Ke, Tsung-Wei, Stella X. Yu, & David Whitney. (2017). Mooney face classification and prediction by learning across tone. 115. 2025–2029. 3 indexed citations

11.

Ke, Tsung-Wei, Michael Maire, & Stella X. Yu. (2016). Neural Multigrid. arXiv (Cornell University). 2 indexed citations

12.

Ke, Tsung-Wei & Tyng-Luh Liu. (2016). Recursive reduction net for large-scale high-dimensional data. 16. 1903–1907. 1 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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