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.
2016IEEE Transactions on Pattern Analysis and Machine Intelligence
This map shows the geographic impact of Jonathan Long'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 Jonathan Long with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan Long more than expected).
This network shows the impact of papers produced by Jonathan Long. 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 Jonathan Long. The network helps show where Jonathan Long may publish in the future.
Co-authorship network
The 18 scholars most cited alongside Jonathan Long, linked wherever they have
co-authored with each other. Click a name or a connecting line to browse the papers they
share.
Border = papers with Jonathan LongLine = papers co-authored togetherJonathan Long links everyone, so they are left out of the graph.
All Works
8 of 8 papers shown
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Work
1
Fully Convolutional Networks for Semantic Segmentationbreakdown →
Jonathan Long is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Biomedical Engineering, having authored 8 papers that have together received 16.3k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (4 papers), Advanced Image and Video Retrieval Techniques (3 papers), Image Retrieval and Classification Techniques (3 papers), Multimodal Machine Learning Applications (3 papers), Domain Adaptation and Few-Shot Learning (2 papers), AI in cancer detection (2 papers), Medical Imaging and Analysis (1 paper) and Generative Adversarial Networks and Image Synthesis (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (9.9k citations), Media Technology (2.0k citations) and Artificial Intelligence (4.1k citations). Jonathan Long has collaborated with scholars based in United States. Frequent co-authors include Trevor Darrell, Evan Shelhamer, Yangqing Jia, Sergey Karayev, Sergio Guadarrama, Ross Girshick, Jeff Donahue, Ning Zhang, Kate Saenko and Sameer Antani. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, arXiv (Cornell University), Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE and eScholarship (California Digital Library).
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.