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SPIN: Hierarchical Segmentation with Subpart Granularity in Natural Images

Autor: Myers-Dean, Josh, Reynolds, Jarek, Price, Brian, Fan, Yifei, Gurari, Danna

Hierarchical segmentation entails creating segmentations at varying levels of granularity. We introduce the first hierarchical semantic segmentation dataset with subpart annotations for natural images, which we call SPIN (SubPartImageNet). We also in

Externí odkaz: http://arxiv.org/abs/2407.09686

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Interpreting COVID Lateral Flow Tests' Results with Foundation Models

Autor: Pandey, Stuti, Myers-Dean, Josh, Reynolds, Jarek, Gurari, Danna

Lateral flow tests (LFTs) enable rapid, low-cost testing for health conditions including Covid, pregnancy, HIV, and malaria. Automated readers of LFT results can yield many benefits including empowering blind people to independently learn about their

Externí odkaz: http://arxiv.org/abs/2404.14990

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Interactive Segmentation for Diverse Gesture Types Without Context

Autor: Myers-Dean, Josh, Fan, Yifei, Price, Brian, Chan, Wilson, Gurari, Danna

Interactive segmentation entails a human marking an image to guide how a model either creates or edits a segmentation. Our work addresses limitations of existing methods: they either only support one gesture type for marking an image (e.g., either cl

Externí odkaz: http://arxiv.org/abs/2307.10518

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Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuning

Autor: Myers-Dean, Josh, Zhao, Yinan, Price, Brian, Cohen, Scott, Gurari, Danna

Generalized few-shot semantic segmentation was introduced to move beyond only evaluating few-shot segmentation models on novel classes to include testing their ability to remember base classes. While the current state-of-the-art approach is based on

Externí odkaz: http://arxiv.org/abs/2112.10982

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