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.
Emerging Properties in Self-Supervised Vision Transformers
20212.5k citationsMathilde Caron, Hugo Touvron et al.2021 IEEE/CVF International Conference on Computer Vision (ICCV)profile →
MDETR - Modulated Detection for End-to-End Multi-Modal Understanding
2021379 citationsMannat Singh, Yann LeCun et al.2021 IEEE/CVF International Conference on Computer Vision (ICCV)profile →
ImageBind One Embedding Space to Bind Them All
2023340 citationsRohit Girdhar, Mannat Singh et al.profile →
Self-Supervised Pretraining of 3D Features on any Point-Cloud
2021151 citationsRohit Girdhar, Armand Joulin et al.2021 IEEE/CVF International Conference on Computer Vision (ICCV)profile →
Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
2023117 citationsMahmoud Assran, Quentin Duval 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 Ishan Misra'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 Ishan Misra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ishan Misra more than expected).
This network shows the impact of papers produced by Ishan Misra. 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 Ishan Misra. The network helps show where Ishan Misra may publish in the future.
Co-authorship network of co-authors of Ishan Misra
This figure shows the co-authorship network connecting the top 25 collaborators of Ishan Misra.
A scholar is included among the top collaborators of Ishan Misra 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 Ishan Misra. Ishan Misra is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Girdhar, Rohit, Mannat Singh, Nikhila Ravi, et al.. (2022). Omnivore: A Single Model for Many Visual Modalities. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 16081–16091.111 indexed citations
10.
Žbontar, Jure, Jing Li, Ishan Misra, Yann LeCun, & Stéphane Deny. (2021). Barlow Twins: Self-Supervised Learning via Redundancy Reduction. International Conference on Machine Learning. 12310–12320.3 indexed citations
DeVries, Terrance, Ishan Misra, Changhan Wang, & Laurens van der Maaten. (2019). Does Object Recognition Work for Everyone. Computer Vision and Pattern Recognition. 52–59.12 indexed citations
16.
Wong, Daniel, Ishan Misra, Michael Kaminsky, et al.. (2018). Mainstream: dynamic stem-sharing for multi-tenant video processing. USENIX Annual Technical Conference. 29–41.44 indexed citations
17.
Huang, Ting-Hao, Francis Ferraro, Nasrin Mostafazadeh, et al.. (2016). Visual Storytelling. 1233–1239.138 indexed citations
18.
Misra, Ishan, C. Lawrence Zitnick, Margaret Mitchell, & Ross Girshick. (2015). Learning Visual Classifiers using Human-centric Annotations.. arXiv (Cornell University).2 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.