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 C. Lawrence Zitnick
Since
Specialization
Citations
This map shows the geographic impact of C. Lawrence Zitnick'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 C. Lawrence Zitnick with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites C. Lawrence Zitnick more than expected).
Fields of papers citing papers by C. Lawrence Zitnick
This network shows the impact of papers produced by C. Lawrence Zitnick. 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 C. Lawrence Zitnick. The network helps show where C. Lawrence Zitnick may publish in the future.
Co-authorship network of co-authors of C. Lawrence Zitnick
This figure shows the co-authorship network connecting the top 25 collaborators of C. Lawrence Zitnick.
A scholar is included among the top collaborators of C. Lawrence Zitnick 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 C. Lawrence Zitnick. C. Lawrence Zitnick is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Johnson, Patricia M., Dana J. Lin, Jure Žbontar, et al.. (2023). Deep Learning Reconstruction Enables Prospectively Accelerated Clinical Knee MRI. Radiology. 307(2). e220425–e220425.69 indexed citations breakdown →
3.
Tran, Richard, Janice Lan, Muhammed Shuaibi, et al.. (2023). The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts. ACS Catalysis. 13(5). 3066–3084.174 indexed citations breakdown →
4.
Rives, Alexander, Joshua Meier, Tom Sercu, et al.. (2021). Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences. 118(15).1429 indexed citations breakdown →
5.
Das, Abhishek, Siddharth Goyal, Thibaut Lavril, et al.. (2021). Open Catalyst 2020 (OC20) Dataset and Community Challenges. ACS Catalysis. 11(10). 6059–6072.440 indexed citations breakdown →
Tian, Yuandong, et al.. (2017). ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games. Neural Information Processing Systems. 30. 2659–2669.25 indexed citations
8.
Bell, Sean, C. Lawrence Zitnick, Kavita Bala, & Ross Girshick. (2016). Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks. 2874–2883.915 indexed citations breakdown →
9.
Huang, Ting-Hao, Francis Ferraro, Nasrin Mostafazadeh, et al.. (2016). Visual Storytelling. 1233–1239.138 indexed citations
10.
Misra, Ishan, C. Lawrence Zitnick, Margaret Mitchell, & Ross Girshick. (2015). Learning Visual Classifiers using Human-centric Annotations.. arXiv (Cornell University).2 indexed citations
Lim, Joseph J., C. Lawrence Zitnick, & Piotr Dollár. (2013). Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection. 3158–3165.292 indexed citations breakdown →
13.
Koppal, Sanjeev J., et al.. (2011). A viewer-centric editor for stereoscopic camera. IEEE Computer Graphics and Applications.3 indexed citations
14.
Yuen, Jenny, C. Lawrence Zitnick, Ce Liu, & Antonio Torralba. (2011). A Framework for Encoding Object-level Image Priors.1 indexed citations
Gemmell, Jim, C. Lawrence Zitnick, Kentaro Toyama, & Steven M. Seitz. (2000). Gaze-awareness for Videoconferencing: A Software Approach. IEEE Multimedia.25 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.