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 Milind Naphade
Since
Specialization
Citations
This map shows the geographic impact of Milind Naphade'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 Milind Naphade with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Milind Naphade more than expected).
This network shows the impact of papers produced by Milind Naphade. 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 Milind Naphade. The network helps show where Milind Naphade may publish in the future.
Co-authorship network of co-authors of Milind Naphade
This figure shows the co-authorship network connecting the top 25 collaborators of Milind Naphade.
A scholar is included among the top collaborators of Milind Naphade 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 Milind Naphade. Milind Naphade is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Naphade, Milind, Zheng Tang, Ming‐Ching Chang, et al.. (2019). The 2019 AI City Challenge.. Computer Vision and Pattern Recognition. 452–460.15 indexed citations
Naphade, Milind, David C. Anastasiu, Anuj Sharma, et al.. (2017). The NVIDIA AI City Challenge. San José State University ScholarWorks (San Jose State University). 1–6.51 indexed citations
Sundaram, Hari, et al.. (2006). Image and Video Retrieval: 5th Internatinoal Conference, CIVR 2006, Tempe, AZ, USA, July 13-15, 2006, Proceedings (Lecture Notes in Computer Science). Springer eBooks.1 indexed citations
Amir, Arnon, Giridharan Iyengar, Milind Naphade, et al.. (2003). IBM Research TRECVID 2004 Video Retrieval System.. TRECVID.91 indexed citations
16.
Naphade, Milind & John R. Smith. (2003). Role of classifiers in multimedia content management. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 5021. 89–89.6 indexed citations
17.
Iyengar, Giridharan, C. Neti, Harriet J. Nock, et al.. (2002). IBM Research TREC 2002 Video Retrieval System.. Text REtrieval Conference.36 indexed citations
18.
Smith, John R., Savitha Srinivasan, Arnon Amir, et al.. (2001). Integrating features, models, and semantics for TREC Video Retrieval. Text REtrieval Conference. 14(16). 240–249.21 indexed citations
19.
Naphade, Milind, I. Kozintsev, & Thomas S. Huang. (2000). Probabilistic Semantic Video Indexing. Neural Information Processing Systems. 13. 967–973.16 indexed citations
20.
Naphade, Milind & Thomas S. Huang. (2000). Stochastic modeling of soundtrack for efficient segmentation and indexing of video. Proceedings of SPIE - The International Society for Optical Engineering. 3972. 168–176.14 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.