Search Results for author: Sara Mousavi

Found 6 papers, 1 papers with code

SLRNet: Semi-Supervised Semantic Segmentation Via Label Reuse for Human Decomposition Images

no code implementations24 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.

Semi-Supervised Semantic Segmentation

Pseudo Pixel-level Labeling for Images with Evolving Content

no code implementations20 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.

Pseudo Label Semantic Segmentation

Collaborative Learning of Semi-Supervised Clustering and Classification for Labeling Uncurated Data

no code implementations9 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.

General Classification

Detecting and Characterizing Bots that Commit Code

2 code implementations2 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.

An Analytical Workflow for Clustering Forensic Images

no code implementations29 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.

Machine-assisted annotation of forensic imagery

no code implementations28 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.

Transfer Learning

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