Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if any of the following hold:
it has ≥500 total citations;
it reaches ≥1.5× the top-1% citation threshold for papers in the same subfield and year (the
threshold is the minimum needed to enter the top 1%, not the average within it);
it reaches the top citation threshold in at least one of its specific research topics.
2017YOLO9000: Better, Faster, Stronger
2016Lecture notes in computer science
2015Real-time grasp detection using convolutional neural networks
This map shows the geographic impact of Joseph Redmon'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 Joseph Redmon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joseph Redmon more than expected).
This network shows the impact of papers produced by Joseph Redmon. 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 Joseph Redmon. The network helps show where Joseph Redmon may publish in the future.
Co-authors
The 4 scholars most cited alongside Joseph Redmon, 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 Joseph RedmonLine = papers co-authored togetherJoseph Redmon links everyone, so they are left out of the graph.
Joseph Redmon is a scholar working on Computer Vision and Pattern Recognition, Control and Systems Engineering, Artificial Intelligence, Neurology and Human-Computer Interaction, having authored 4 papers that have together received 17.8k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (3 papers), Advanced Image and Video Retrieval Techniques (2 papers), Hand Gesture Recognition Systems (1 paper), Domain Adaptation and Few-Shot Learning (1 paper), Soft Robotics and Applications (1 paper), Video Surveillance and Tracking Methods (1 paper), Brain Tumor Detection and Classification (1 paper) and Robot Manipulation and Learning (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (12.3k citations), Media Technology (1.9k citations), Industrial and Manufacturing Engineering (1.8k citations), Artificial Intelligence (3.8k citations) and Aerospace Engineering (2.4k citations). Joseph Redmon has collaborated with scholars based in United States. Frequent co-authors include Ali Farhadi, Mohammad Rastegari, Vicente Ordóñez and Anelia Angelova. Their work appears in journals such as Communications of the ACM and Lecture notes in computer science.
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.