73 citations · 75 across the 5 of their papers we have counts for
6 papers
SLRNet: Semi-Supervised Semantic Segmentation Via Label Reuse for Human Decomposition Images
Sara Mousavi, Zhenning Yang, Kelley Cross +2
Semantic segmentation is a challenging computer vision task demanding a significant amount of pixel-level annotated data. Producing such data is a time-consuming and costly process…
Pseudo Pixel-level Labeling for Images with Evolving Content
Sara Mousavi, Zhenning Yang, Kelley Cross +2
Annotating images for semantic segmentation requires intense manual labor and is a time-consuming and expensive task especially for domains with a scarcity of experts, such as Fore…
Detecting and Characterizing Bots that Commit Code
Tapajit Dey, Sara Mousavi, Eduardo Ponce +4
Background: Some developer activity traditionally performed manually, such as making code commits, opening, managing, or closing issues is increasingly subject to automation in man…
Collaborative Learning of Semi-Supervised Clustering and Classification for Labeling Uncurated Data
Sara Mousavi, Dylan Lee, Tatianna Griffin +2
Domain-specific image collections present potential value in various areas of science and business but are often not curated nor have any way to readily extract relevant content. T…
An Analytical Workflow for Clustering Forensic Images
Sara Mousavi, Dylan Lee, Tatianna Griffin +2
Large collections of images, if curated, drastically contribute to the quality of research in many domains. Unsupervised clustering is an intuitive, yet effective step towards cura…
Machine-assisted annotation of forensic imagery
Sara Mousavi, Ramin Nabati, Megan Kleeschulte +1
Image collections, if critical aspects of image content are exposed, can spur research and practical applications in many domains. Supervised machine learning may be the only feasi…