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.
3D Semantic Parsing of Large-Scale Indoor Spaces
20161.2k citationsIro Armeni, Ozan Şener et al.Apollo (University of Cambridge)profile →
The THUMOS challenge on action recognition for videos “in the wild”
2016325 citationsHaroon Idrees, Amir Zamir et al.Computer Vision and Image Understandingprofile →
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 Amir Zamir'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 Amir Zamir with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amir Zamir more than expected).
This network shows the impact of papers produced by Amir Zamir. 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 Amir Zamir. The network helps show where Amir Zamir may publish in the future.
Co-authorship network of co-authors of Amir Zamir
This figure shows the co-authorship network connecting the top 25 collaborators of Amir Zamir.
A scholar is included among the top collaborators of Amir Zamir 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 Amir Zamir. Amir Zamir is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zamir, Amir, et al.. (2022). 3D Common Corruptions and Data Augmentation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 18941–18952.41 indexed citations
4.
Vinker, Yael, Roman Bachmann, Amit H. Bermano, et al.. (2022). CLIPasso. ACM Transactions on Graphics. 41(4). 1–11.94 indexed citations
5.
Sax, Alexander F., F Lewis, Iro Armeni, et al.. (2020). Robust Policies via Mid-Level Visual Representations: An Experimental Study in Manipulation and Navigation. arXiv (Cornell University). 2328–2346.1 indexed citations
6.
Standley, Trevor, Amir Zamir, Dawn Chen, et al.. (2020). Which Tasks Should Be Learned Together in Multi-task Learning?. International Conference on Machine Learning. 1. 9120–9132.99 indexed citations
Sax, Alexander F., et al.. (2019). Side-Tuning: Network Adaptation via Additive Side Networks. arXiv (Cornell University).2 indexed citations
9.
Sax, Alexander F., et al.. (2018). Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Active Tasks.. arXiv (Cornell University).7 indexed citations
10.
Bao, Yajie, Yang Li, Shao‐Lun Huang, et al.. (2018). An Information-Theoretic Metric of Transferability for Task Transfer Learning.3 indexed citations
11.
Zamir, Amir, Te-Lin Wu, Lin Sun, et al.. (2017). Feedback Networks. Computer Vision and Pattern Recognition.48 indexed citations
12.
Zamir, Amir, Te-Lin Wu, Lin Sun, et al.. (2017). Feedback Networks. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 1808–1817.87 indexed citations
13.
Szeliski, Richard, Mubarak Shah, Luc Van Gool, Asaad Hakeem, & Amir Zamir. (2016). Large-Scale Visual Geo-Localization. DIAL (Catholic University of Leuven).21 indexed citations
14.
Armeni, Iro, Ozan Şener, Amir Zamir, et al.. (2016). 3D Semantic Parsing of Large-Scale Indoor Spaces. Apollo (University of Cambridge). 1534–1543.1170 indexed citations breakdown →
15.
Idrees, Haroon, Amir Zamir, Yu–Gang Jiang, et al.. (2016). The THUMOS challenge on action recognition for videos “in the wild”. Computer Vision and Image Understanding. 155. 1–23.325 indexed citations breakdown →
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.