1 code implementation • 24 Feb 2022 • Sara Mousavi, Zhenning Yang, Kelley Cross, Dawnie Steadman, Audris Mockus
We evaluate our method on a large dataset of human decomposition images and find that our method, while conceptually simple, outperforms state-of-the-art consistency and pseudo-labeling-based methods for the segmentation of this dataset.
no code implementations • 20 May 2021 • Sara Mousavi, Zhenning Yang, Kelley Cross, Dawnie Steadman, Audris Mockus
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 Forensic Anthropology.
no code implementations • 9 Mar 2020 • Sara Mousavi, Dylan Lee, Tatianna Griffin, Dawnie Steadman, Audris Mockus
In our experiment comparing manual labeling with labeling conducted with the support of Plud, we found that it reduces the time needed to label data and produces highly accurate models for this new domain.
1 code implementation • 2 Mar 2020 • Tapajit Dey, Sara Mousavi, Eduardo Ponce, Tanner Fry, Bogdan Vasilescu, Anna Filippova, Audris Mockus
Background: Some developer activity traditionally performed manually, such as making code commits, opening, managing, or closing issues is increasingly subject to automation in many OSS projects.
no code implementations • 29 Dec 2019 • Sara Mousavi, Dylan Lee, Tatianna Griffin, Dawnie Steadman, Audris Mockus
Large collections of images, if curated, drastically contribute to the quality of research in many domains.
no code implementations • 28 Feb 2019 • Sara Mousavi, Ramin Nabati, Megan Kleeschulte, Audris Mockus
In the case of a large forensic collection, we are aiming to annotate, neither the complete annotation nor the large training samples can be feasibly produced.