2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.CV2021
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…
cs.LG2020
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…
cs.CV2019★ 2 cited
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…