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.
GroupViT: Semantic Segmentation Emerges from Text Supervision
2022243 citationsJiarui Xu, Shalini De Mello et al.2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)profile →
Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models
2023156 citationsJiarui Xu, Sifei Liu et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Wonmin Byeon'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 Wonmin Byeon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wonmin Byeon more than expected).
This network shows the impact of papers produced by Wonmin Byeon. 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 Wonmin Byeon. The network helps show where Wonmin Byeon may publish in the future.
Co-authorship network of co-authors of Wonmin Byeon
This figure shows the co-authorship network connecting the top 25 collaborators of Wonmin Byeon.
A scholar is included among the top collaborators of Wonmin Byeon 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 Wonmin Byeon. Wonmin Byeon is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Lee, Seung Hyun, Wonmin Byeon, Sang Ho Yoon, et al.. (2022). Sound-Guided Semantic Image Manipulation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 3367–3376.29 indexed citations
10.
Yu, Zhiding, Wonmin Byeon, Sifei Liu, et al.. (2021). Coupled Segmentation and Edge Learning via Dynamic Graph Propagation. CaltechAUTHORS (California Institute of Technology). 34.5 indexed citations
11.
Su, Jiahao, Wonmin Byeon, Jean Kossaifi, et al.. (2020). Convolutional Tensor-Train LSTM for Spatio-temporal Learning. CaltechAUTHORS (California Institute of Technology). 33. 13714–13726.5 indexed citations
12.
Vorontsov, Eugene, Pavlo Molchanov, Wonmin Byeon, et al.. (2019). Boosting segmentation with weak supervision from image-to-image translation..4 indexed citations
Byeon, Wonmin, Qin Wang, Rupesh K. Srivastava, & Petros Koumoutsakos. (2017). Fully Context-Aware Video Prediction.. arXiv (Cornell University).3 indexed citations
15.
Byeon, Wonmin. (2016). Image Analysis with Long Short-Term Memory Recurrent Neural Networks. Publication Server of Kaiserslautern University of Technology (Kaiserslautern University of Technology).1 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.