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.
Photobook: Content-based manipulation of image databases
19961.1k citationsAlex Pentland, Stan Sclaroff et al.profile →
Semi-Supervised Domain Adaptation via Minimax Entropy
2019387 citationsKuniaki Saito, Donghyun Kim et al.profile →
Top-Down Neural Attention by Excitation Backprop
2017372 citationsJianming Zhang, Sarah Adel Bargal et al.profile →
Minimum Barrier Salient Object Detection at 80 FPS
This map shows the geographic impact of Stan Sclaroff'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 Stan Sclaroff with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stan Sclaroff more than expected).
This network shows the impact of papers produced by Stan Sclaroff. 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 Stan Sclaroff. The network helps show where Stan Sclaroff may publish in the future.
Co-authorship network of co-authors of Stan Sclaroff
This figure shows the co-authorship network connecting the top 25 collaborators of Stan Sclaroff.
A scholar is included among the top collaborators of Stan Sclaroff 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 Stan Sclaroff. Stan Sclaroff is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Saito, Kuniaki, Donghyun Kim, Stan Sclaroff, & Kate Saenko. (2020). Universal Domain Adaptation through Self Supervision. neural information processing systems. 33. 16282–16292.2 indexed citations
3.
Bargal, Sarah Adel, et al.. (2019). Are CNN Predictions based on Reasonable Evidence. Computer Vision and Pattern Recognition. 67–70.1 indexed citations
Xu, Huijuan, Kun He, Leonid Sigal, Stan Sclaroff, & Kate Saenko. (2018). Text-to-Clip Video Retrieval with Early Fusion and Re-Captioning.. arXiv (Cornell University).8 indexed citations
6.
Çakir, Fatih, Sarah Adel Bargal, & Stan Sclaroff. (2016). Online supervised hashing. Computer Vision and Image Understanding. 156. 162–173.42 indexed citations
7.
Bai, Qinxun, Steven Rosenberg, Zheng Wu, & Stan Sclaroff. (2016). Differential geometric regularization for supervised learning of classifiers. OpenBU/Boston University Institutional Repository (Boston University). 1879–1888.1 indexed citations
Bai, Qinxun, Henry Lam, & Stan Sclaroff. (2014). A Bayesian Framework for Online Classifier Ensemble. International Conference on Machine Learning. 1584–1592.4 indexed citations
Dreuw, Philippe, Carol Neidle, Vassilis Athitsos, Stan Sclaroff, & Hermann Ney. (2008). Benchmark Databases for Video-Based Automatic Sign Language Recognition. Language Resources and Evaluation.66 indexed citations
Isidoro, John & Stan Sclaroff. (2003). Stochastic Refinement of the Visual Hull to Satisfy Photometric and Silhouette Consistency Constraints. OpenBU/Boston University Institutional Repository (Boston University).43 indexed citations
15.
Athitsos, Vassilis & Stan Sclaroff. (2003). Database Indexing Methods for 3D Hand Pose Estimation. OpenBU/Boston University Institutional Repository (Boston University).
Rosales, Rómer & Stan Sclaroff. (2001). Learning Body Pose via Specialized Maps. Neural Information Processing Systems. 14. 1263–1270.68 indexed citations
18.
Sclaroff, Stan & John Isidoro. (1998). Active blobs. 1146.96 indexed citations
Essa, Irfan, Stan Sclaroff, & Alex Pentland. (1993). Physically-based Modeling for Graphics and Vision.10 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.