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.
Countries citing papers authored by Tamara L. Berg
Since
Specialization
Citations
This map shows the geographic impact of Tamara L. Berg'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 Tamara L. Berg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tamara L. Berg more than expected).
This network shows the impact of papers produced by Tamara L. Berg. 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 Tamara L. Berg. The network helps show where Tamara L. Berg may publish in the future.
Co-authorship network of co-authors of Tamara L. Berg
This figure shows the co-authorship network connecting the top 25 collaborators of Tamara L. Berg.
A scholar is included among the top collaborators of Tamara L. Berg 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 Tamara L. Berg. Tamara L. Berg is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Lei, Jie, Tamara L. Berg, & Mohit Bansal. (2021). Detecting Moments and Highlights in Videos via Natural Language Queries. Neural Information Processing Systems. 34.41 indexed citations
3.
Li, Linjie, Jie Lei, Zhe Gan, et al.. (2021). VALUE: A Multi-Task Benchmark for Video-and-Language Understanding Evaluation. arXiv (Cornell University).1 indexed citations
4.
Lei, Jie, Linjie Li, Luowei Zhou, et al.. (2021). Less is More: CLIPBERT for Video-and-Language Learning via Sparse Sampling. 7327–7337.372 indexed citations breakdown →
Kazemzadeh, Sahar, et al.. (2014). ReferItGame: Referring to Objects in Photographs of Natural Scenes. 787–798.570 indexed citations breakdown →
10.
Yamaguchi, Kota, M. Hadi Kiapour, Luis E. Ortiz, & Tamara L. Berg. (2014). Retrieving Similar Styles to Parse Clothing. IEEE Transactions on Pattern Analysis and Machine Intelligence. 37(5). 1028–1040.81 indexed citations
Кузнецова, Полина, Vicente Ordóñez, Alexander Berg, Tamara L. Berg, & Yejin Choi. (2013). Generalizing Image Captions for Image-Text Parallel Corpus. Meeting of the Association for Computational Linguistics. 2. 790–796.49 indexed citations
13.
Kulkarni, Girish, Vicente Ordóñez, Siming Li, et al.. (2013). BabyTalk: Understanding and Generating Simple Image Descriptions. IEEE Transactions on Pattern Analysis and Machine Intelligence. 35(12). 2891–2903.525 indexed citations breakdown →
14.
Кузнецова, Полина, Vicente Ordóñez, Alexander Berg, Tamara L. Berg, & Yejin Choi. (2012). Collective Generation of Natural Image Descriptions. Meeting of the Association for Computational Linguistics. 359–368.208 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.