Liang-Chieh Chen
Impact in
- Computer Vision and Pattern Recognition top 0.02%
- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Video Surveillance and Tracking Methods
- Multimodal Machine Learning Applications
- Medical Image Segmentation Techniques
- Media Technology top 0.05%
- Remote-Sensing Image Classification
Papers in
-
- Advanced Neural Network Applications 15
- Advanced Image and Video Retrieval Techniques 14
- Visual Attention and Saliency Detection 4
- Video Surveillance and Tracking Methods 3
- Advanced Vision and Imaging 3
- Multimodal Machine Learning Applications 2
-
- Domain Adaptation and Few-Shot Learning 7
- Co-authors
- Alan Yuille (11 shared papers)George Papandreou (5 shared papers)Kevin Murphy (2 shared papers)Iasonas Kokkinos (1 shared paper)Hartwig Adam (10 shared papers)Siyuan Qiao (5 shared papers)Jiang Wang (1 shared paper)Wei Xu (1 shared paper)
- Journals
- International Journal of Computer Vision (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2 papers)arXiv (Cornell University) (4 papers)PubMed (2 papers)
- Partner nations
- United StatesCanadaSouth Korea
In The Last Decade
Liang-Chieh Chen
25 papers receiving 18.5k citations
Liang-Chieh Chen's Hit Papers
Peers
Comparison fields: 5 of 189
- Computer Vision and Pattern Recognition 12.8k
- Media Technology 2.8k
- Artificial Intelligence 5.3k
- Industrial and Manufacturing Engineering 997
- Radiology, Nuclear Medicine and Imaging 2.2k
Countries citing papers authored by Liang-Chieh Chen
This map shows the geographic impact of Liang-Chieh Chen'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 Liang-Chieh Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Liang-Chieh Chen more than expected).
Fields of papers citing papers by Liang-Chieh Chen
This network shows the impact of papers produced by Liang-Chieh Chen. 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 Liang-Chieh Chen. The network helps show where Liang-Chieh Chen may publish in the future.
Co-authors
The 25 scholars most cited alongside Liang-Chieh Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs Hit paper breakdown → | 2017 | 14377 |
| 2 | Attention to Scale: Scale-Aware Semantic Image Segmentation Hit paper breakdown → | 2016 | 951 |
| 3 | DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution Hit paper breakdown → | 2021 | 682 |
| 4 | Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation Hit paper breakdown → | 2019 | 628 |
| 5 | Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation Hit paper breakdown → | 2015 | 602 |
| 6 | Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation Hit paper breakdown → | 2018 | 386 |
| 7 | Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation Hit paper breakdown → | 2020 | 380 |
| 8 | MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers Hit paper breakdown → | 2021 | 329 |
| 9 | 2018 | 225 | |
| 10 | 2021 | 94 | |
| 11 | 2019 | 79 | |
| 12 | 2022 | 55 | |
| 13 | 2019 | 49 | |
| 14 | 2014 | 48 | |
| 15 | 2022 | 22 | |
| 16 | 2024 | 12 | |
| 17 | 2014 | 11 | |
| 18 | 2017 | 11 | |
| 19 | 2013 | 8 | |
| 20 | 2024 | 6 |
About Liang-Chieh Chen
Liang-Chieh Chen is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Aerospace Engineering, Computational Mechanics and Industrial and Manufacturing Engineering, having authored 25 papers that have together received 19.0k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (15 papers), Advanced Image and Video Retrieval Techniques (14 papers), Domain Adaptation and Few-Shot Learning (7 papers), Visual Attention and Saliency Detection (4 papers), Video Surveillance and Tracking Methods (3 papers), Robotics and Sensor-Based Localization (3 papers), Advanced Vision and Imaging (3 papers) and Multimodal Machine Learning Applications (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (12.8k citations), Media Technology (2.8k citations), Artificial Intelligence (5.3k citations), Industrial and Manufacturing Engineering (997 citations) and Radiology, Nuclear Medicine and Imaging (2.2k citations). Liang-Chieh Chen has collaborated with scholars based in United States, Canada and South Korea. Frequent co-authors include Alan Yuille, George Papandreou, Kevin Murphy, Iasonas Kokkinos, Hartwig Adam, Siyuan Qiao, Jiang Wang, Wei Xu, Yi Yang and Yukun Zhu. Their work appears in journals such as International Journal of Computer Vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), arXiv (Cornell University) and PubMed.
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.