Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
201714.4k citationsLiang-Chieh Chen, George Papandreou et al.IEEE Transactions on Pattern Analysis and Machine Intelligenceprofile →
Attention to Scale: Scale-Aware Semantic Image Segmentation
2016951 citationsLiang-Chieh Chen, Yi Yang et al.profile →
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution
2021682 citationsSiyuan Qiao, Liang-Chieh Chen et al.profile →
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
2019628 citationsChenxi Liu, Liang-Chieh Chen et al.profile →
Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation
2015602 citationsGeorge Papandreou, Liang-Chieh Chen et al.profile →
Countries citing papers authored by Liang-Chieh Chen
Since
Specialization
Citations
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-authorship network of co-authors of Liang-Chieh Chen
This figure shows the co-authorship network connecting the top 25 collaborators of Liang-Chieh Chen.
A scholar is included among the top collaborators of Liang-Chieh Chen 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 Liang-Chieh Chen. Liang-Chieh Chen is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kim, Dahun, Jun Xie, Huiyu Wang, et al.. (2022). TubeFormer-DeepLab: Video Mask Transformer. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).22 indexed citations
Qiao, Siyuan, Liang-Chieh Chen, & Alan Yuille. (2021). DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution. 10208–10219.682 indexed citations breakdown →
9.
Chen, Liang-Chieh, Raphael Gontijo Lopes, Bowen Cheng, et al.. (2020). Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation.. arXiv (Cornell University).2 indexed citations
10.
Chen, Liang-Chieh, Raphael Gontijo Lopes, Bowen Cheng, et al.. (2020). Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation. arXiv (Cornell University).5 indexed citations
11.
Cheng, Bowen, Maxwell D. Collins, Yukun Zhu, et al.. (2020). Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation. 12472–12482.380 indexed citations breakdown →
Chen, Liang-Chieh, George Papandreou, Iasonas Kokkinos, Kevin Murphy, & Alan Yuille. (2017). DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence. 40(4). 834–848.14377 indexed citations breakdown →
16.
Chen, Liang-Chieh, Yi Yang, Jiang Wang, Wei Xu, & Alan Yuille. (2016). Attention to Scale: Scale-Aware Semantic Image Segmentation. 3640–3649.951 indexed citations breakdown →
17.
Papandreou, George, Liang-Chieh Chen, Kevin Murphy, & Alan Yuille. (2015). Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation. 1742–1750.602 indexed citations breakdown →
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.