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.
Unsupervised Visual Representation Learning by Context Prediction
20151.4k citationsCarl Doersch, Abhinav Gupta et al.profile →
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
20171.3k citationsChen Sun, Abhinav Shrivastava et al.profile →
Ensemble of exemplar-SVMs for object detection and beyond
2011567 citationsTomasz Malisiewicz, Abhinav Gupta et al.Figshareprofile →
Unsupervised Learning of Visual Representations Using Videos
This map shows the geographic impact of Abhinav Gupta'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 Abhinav Gupta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Abhinav Gupta more than expected).
This network shows the impact of papers produced by Abhinav Gupta. 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 Abhinav Gupta. The network helps show where Abhinav Gupta may publish in the future.
Co-authorship network of co-authors of Abhinav Gupta
This figure shows the co-authorship network connecting the top 25 collaborators of Abhinav Gupta.
A scholar is included among the top collaborators of Abhinav Gupta 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 Abhinav Gupta. Abhinav Gupta is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Chaplot, Devendra Singh, Dhiraj Gandhi, Abhinav Gupta, & Russ R. Salakhutdinov. (2020). Object Goal Navigation using Goal-Oriented Semantic Exploration. Neural Information Processing Systems. 33. 4247–4258.20 indexed citations
6.
WHITNEY, WILLIAM F., et al.. (2020). Dynamics-Aware Embeddings. arXiv (Cornell University).1 indexed citations
7.
Marino, Kenneth, Abhinav Gupta, Rob Fergus, & Arthur Szlam. (2019). Hierarchical RL Using an Ensemble of Proprioceptive Periodic Policies. International Conference on Learning Representations.2 indexed citations
8.
Purushwalkam, Senthil, Abhinav Gupta, Danny M. Kaufman, & Bryan Russell. (2019). Bounce and Learn: Modeling Scene Dynamics with Real-World Bounces. arXiv (Cornell University).2 indexed citations
Malisiewicz, Tomasz, Abhinav Shrivastava, Abhinav Gupta, & Alexei A. Efros. (2012). Exemplar-SVMs for visual object detection, label transfer and image retrieval. International Conference on Machine Learning. 7–8.6 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.