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.
NISP: Pruning Networks Using Neuron Importance Score Propagation
2018470 citationsVlad I. Morariu, Xintong Han et al.profile →
Learning Rich Features for Image Manipulation Detection
2018438 citationsPeng Zhou, Xintong Han et al.profile →
Two-Stream Neural Networks for Tampered Face Detection
2017384 citationsPeng Zhou, Xintong Han et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Vlad I. Morariu
Since
Specialization
Citations
This map shows the geographic impact of Vlad I. Morariu'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 Vlad I. Morariu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vlad I. Morariu more than expected).
This network shows the impact of papers produced by Vlad I. Morariu. 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 Vlad I. Morariu. The network helps show where Vlad I. Morariu may publish in the future.
Co-authorship network of co-authors of Vlad I. Morariu
This figure shows the co-authorship network connecting the top 25 collaborators of Vlad I. Morariu.
A scholar is included among the top collaborators of Vlad I. Morariu 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 Vlad I. Morariu. Vlad I. Morariu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Gu, Jiuxiang, Jason Kuen, Vlad I. Morariu, et al.. (2021). UniDoc: Unified Pretraining Framework for Document Understanding. Neural Information Processing Systems. 34.22 indexed citations
Zhou, Peng, Xintong Han, Vlad I. Morariu, & Larry S. Davis. (2018). Learning Rich Features for Image Manipulation Detection. 1053–1061.438 indexed citations breakdown →
12.
Wang, Yaming, Vlad I. Morariu, & Larry S. Davis. (2016). Weakly-supervised Discriminative Patch Learning via CNN for Fine-grained Recognition.. arXiv (Cornell University).6 indexed citations
Luisier, Florian, W.D. Andrews, Guangnan Ye, et al.. (2014). BBN VISER TRECVID 2014 Multimedia Event Detection and Multimedia Event Recounting Systems.. TRECVID.6 indexed citations
15.
Natarajan, Pradeep, Shuang Wu, Florian Luisier, et al.. (2013). BBN VISER TRECVID 2013 Multimedia Event Detection and Multimedia Event Recounting Systems. Journal of International Crisis and Risk Communication Research.14 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.