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.
Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
2020380 citationsBowen Cheng, Maxwell D. Collins et al.profile →
Citations per year, relative to Bowen Cheng Bowen Cheng (= 1×)
peers
Guangliang Cheng
Countries citing papers authored by Bowen Cheng
Since
Specialization
Citations
This map shows the geographic impact of Bowen Cheng'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 Bowen Cheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bowen Cheng more than expected).
This network shows the impact of papers produced by Bowen Cheng. 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 Bowen Cheng. The network helps show where Bowen Cheng may publish in the future.
Co-authorship network of co-authors of Bowen Cheng
This figure shows the co-authorship network connecting the top 25 collaborators of Bowen Cheng.
A scholar is included among the top collaborators of Bowen Cheng 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 Bowen Cheng. Bowen Cheng is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Cheng, Bowen, Omkar Parkhi, & Alexander Kirillov. (2022). Pointly-Supervised Instance Segmentation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2607–2616.86 indexed citations
3.
Cheng, Bowen, Ross Girshick, Piotr Dollár, Alexander C. Berg, & Alexander Kirillov. (2021). Boundary IoU: Improving Object-Centric Image Segmentation Evaluation. 15329–15337.220 indexed citations breakdown →
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
6.
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
7.
Zhang, Xiaofan, Cong Hao, Jiachen Li, et al.. (2020). SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems. arXiv (Cornell University). 2. 216–229.11 indexed citations
8.
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 →
9.
Cheng, Bowen, Bin Xiao, Jingdong Wang, et al.. (2019). Bottom-up Higher-Resolution Networks for Multi-Person Pose Estimation. arXiv (Cornell University).22 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.