Tianjian Meng

1.1k citations
4 papers · 587 indexed · 2 hit papers · h-index 3
Topics
Domain Adaptation and Few-Shot Learning (2 papers)Advanced Neural Network Applications (2 papers)Advanced Image and Video Retrieval Techniques (1 paper)
Journals
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)arXiv (Cornell University)
Partner nations
United StatesCanadaChina

In The Last Decade

Tianjian Meng

4 papers receiving 568 citations

Hit Papers

DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D O...202120262022202420222021100200300

Peers

Tianjian Meng
Comparison fields: 5 of 61
  • Computer Vision and Pattern Recognition 459
  • Artificial Intelligence 193
  • Aerospace Engineering 133
  • Environmental Engineering 59
  • Geology 47
Replace Yingwei Li with:
Yingwei Li United States
Qingqiu Huang China
Yohann Cabon South Korea
Holger Caesar Netherlands
Mingyu Ding China
Tengteng Huang China
François Rameau South Korea
Wenwei Zhang China
Anton Konushin Russia
Jiaming Zhang Germany
Tianjian Meng relative to Yingwei Li United States Yingwei Li's profile →
Citations per field
00.5×1.5×1.8×
Yingwei Li · 1×
Citations per year

Countries citing papers authored by Tianjian Meng

Since Specialization
Citations

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

Fields of papers citing papers by Tianjian Meng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tianjian Meng

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

All Works

4 of 4 papers shown
#WorkIndexed citations
1
DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detectionbreakdown →
301
2
Spatiotemporal Contrastive Video Representation Learningbreakdown →
259
3 2
4 25

About Tianjian Meng

Tianjian Meng is a scholar working on Computer Vision and Pattern Recognition, Neurology and Artificial Intelligence, having authored 4 papers that have together received 587 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (2 papers), Advanced Neural Network Applications (2 papers) and Advanced Image and Video Retrieval Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (459 citations), Geology (47 citations) and Instrumentation (23 citations). Tianjian Meng has collaborated with scholars based in United States, Canada and China. Frequent co-authors include Yin Cui, Boqing Gong, Rui Qian, Ming–Hsuan Yang, Serge Belongie, Huisheng Wang, Daiyi Peng, Alan Yuille, Yingwei Li and Yifeng Lu. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and arXiv (Cornell University).

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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