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.
VideoBERT: A Joint Model for Video and Language Representation Learning
2019658 citationsChen Sun, Austin Myers et al.profile →
Im2Calories: Towards an Automated Mobile Vision Food Diary
2015320 citationsAustin Myers, Nick Johnston et al.profile →
Peers
Austin Myers
Comparison fields: 5 of 93
Computer Vision and Pattern Recognition728
Artificial Intelligence446
Biomedical Engineering215
Public Health, Environmental and Occupational Health185
This map shows the geographic impact of Austin Myers'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 Austin Myers with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Austin Myers more than expected).
This network shows the impact of papers produced by Austin Myers. 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 Austin Myers. The network helps show where Austin Myers may publish in the future.
Co-authorship network of co-authors of Austin Myers
This figure shows the co-authorship network connecting the top 25 collaborators of Austin Myers.
A scholar is included among the top collaborators of Austin Myers 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 Austin Myers. Austin Myers is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
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.