Tuan-Hung Vu

2.0k total citations
8 papers, 288 citations indexed

About

Tuan-Hung Vu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Automotive Engineering. According to data from OpenAlex, Tuan-Hung Vu has authored 8 papers receiving a total of 288 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Vision and Pattern Recognition, 6 papers in Artificial Intelligence and 1 paper in Automotive Engineering. Recurrent topics in Tuan-Hung Vu's work include Domain Adaptation and Few-Shot Learning (6 papers), Multimodal Machine Learning Applications (5 papers) and Advanced Neural Network Applications (3 papers). Tuan-Hung Vu is often cited by papers focused on Domain Adaptation and Few-Shot Learning (6 papers), Multimodal Machine Learning Applications (5 papers) and Advanced Neural Network Applications (3 papers). Tuan-Hung Vu collaborates with scholars based in France and United States. Tuan-Hung Vu's co-authors include Raoul de Charette, Patrick Pérez, Émilie Wirbel, Maximilian Jaritz, Matthieu Cord, Patrick Pérez, Arthur Douillard, Andrei Bursuc, Alexandre Boulch and Gilles Puy and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 IEEE/CVF International Conference on Computer Vision (ICCV) and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

In The Last Decade

Tuan-Hung Vu

8 papers receiving 285 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tuan-Hung Vu France	7	205	121	53	50	42	8	288
Maximilian Jaritz France	4	309 1.5×	75 0.6×	66 1.2×	50 1.0×	99 2.4×	4	379
Shiyi Lan United States	5	208 1.0×	52 0.4×	55 1.0×	100 2.0×	65 1.5×	6	310
Émilie Wirbel France	7	347 1.7×	93 0.8×	68 1.3×	50 1.0×	112 2.7×	12	439
Thang Vu South Korea	7	177 0.9×	57 0.5×	63 1.2×	86 1.7×	47 1.1×	12	303
Jiantao Gao China	8	224 1.1×	35 0.3×	60 1.1×	115 2.3×	77 1.8×	16	329
Xiwu Chen China	3	234 1.1×	96 0.8×	58 1.1×	93 1.9×	51 1.2×	5	338
Qiguang Miao China	10	153 0.7×	48 0.4×	67 1.3×	86 1.7×	77 1.8×	45	294
Lue Fan China	7	219 1.1×	28 0.2×	42 0.8×	46 0.9×	114 2.7×	12	291
Yueh-Cheng Liu Taiwan	6	151 0.7×	61 0.5×	31 0.6×	45 0.9×	52 1.2×	7	223
Oliver Wasenmüller Germany	9	189 0.9×	26 0.2×	23 0.4×	29 0.6×	122 2.9×	29	280

Countries citing papers authored by Tuan-Hung Vu

Since Specialization

Citations

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

Fields of papers citing papers by Tuan-Hung Vu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tuan-Hung Vu. 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 Tuan-Hung Vu. The network helps show where Tuan-Hung Vu may publish in the future.

Co-authorship network of co-authors of Tuan-Hung Vu

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

8 of 8 papers shown

1.

Vu, Tuan-Hung, et al.. (2024). A Simple Recipe for Language-Guided Domain Generalized Segmentation. 23428–23437. 8 indexed citations

2.

Boulch, Alexandre, et al.. (2024). SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation. 421–431. 6 indexed citations

3.

Vu, Tuan-Hung, et al.. (2023). Cross-task Attention Mechanism for Dense Multi-task Learning. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 2328–2337. 19 indexed citations

4.

Douillard, Arthur, et al.. (2022). Multi-Head Distillation for Continual Unsupervised Domain Adaptation in Semantic Segmentation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 14 indexed citations

5.

Jaritz, Maximilian, Tuan-Hung Vu, Raoul de Charette, Émilie Wirbel, & Patrick Pérez. (2022). Cross-Modal Learning for Domain Adaptation in 3D Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(2). 1533–1544. 39 indexed citations

6.

Vu, Tuan-Hung, et al.. (2021). Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 9052–9061. 36 indexed citations

7.

Jaritz, Maximilian, Tuan-Hung Vu, Raoul de Charette, Émilie Wirbel, & Patrick Pérez. (2020). xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation. 12602–12611. 150 indexed citations

8.

Wirbel, Émilie, et al.. (2020). VRUNet: Multi-Task Learning Model for Intent Prediction of Vulnerable Road Users. Electronic Imaging. 32(16). 109–1. 16 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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