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.
A Closer Look at Spatiotemporal Convolutions for Action Recognition
20182.0k citationsDu Tran, Heng Wang et al.profile →
Video Classification With Channel-Separated Convolutional Networks
2019375 citationsDu Tran, Heng Wang et al.profile →
What Makes Training Multi-Modal Classification Networks Hard?
2020296 citationsWei‐Yao Wang, Du Tran 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 Du Tran'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 Du Tran with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Du Tran more than expected).
This network shows the impact of papers produced by Du Tran. 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 Du Tran. The network helps show where Du Tran may publish in the future.
Co-authorship network of co-authors of Du Tran
This figure shows the co-authorship network connecting the top 25 collaborators of Du Tran.
A scholar is included among the top collaborators of Du Tran 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 Du Tran. Du Tran is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Alwassel, Humam, Dhruv Mahajan, Bruno Korbar, et al.. (2020). Self-Supervised Learning by Cross-Modal Audio-Video Clustering. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 33. 9758–9770.16 indexed citations
Wang, Wei‐Yao, Du Tran, & Matt Feiszli. (2019). What Makes Training Multi-Modal Networks Hard?. arXiv (Cornell University).10 indexed citations
11.
Zhu, Linchao, Laura Sevilla-Lara, Du Tran, et al.. (2019). FASTER Recurrent Networks for Video Classification.. arXiv (Cornell University).1 indexed citations
Korbar, Bruno, Du Tran, & Lorenzo Torresani. (2018). Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization. arXiv (Cornell University). 31. 7763–7774.71 indexed citations
14.
Korbar, Bruno, Du Tran, & Lorenzo Torresani. (2018). Co-Training of Audio and Video Representations from Self-Supervised Temporal Synchronization. arXiv (Cornell University).27 indexed citations
Tran, Du, Heng Wang, Lorenzo Torresani, et al.. (2018). A Closer Look at Spatiotemporal Convolutions for Action Recognition. 6450–6459.2005 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.