Contributo in atti di convegno, 2021, ENG, 10.48550/arXiv.2112.12702

TagLab: A human-centric AI system for interactive semantic segmentation

Pavoni G.; Corsini M.; Ponchio F.; Muntoni A.; Cignoni P.

CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy

Fully automatic semantic segmentation of highly specific semantic classes and complex shapes may not meet the accuracy standards demanded by scientists. In such cases, human-centered AI solutions, able to assist operators while preserving human control over complex tasks, are a good trade-off to speed up image labeling while maintaining high accuracy levels. TagLab is an open-source AI-assisted software for annotating large orthoimages which takes advantage of different degrees of automation; it speeds up image annotation from scratch through assisted tools, creates custom fully automatic semantic segmentation models, and, finally, allows the quick edits of automatic predictions. Since the orthoimages analysis applies to several scientific disciplines, TagLab has been designed with a flexible labeling pipeline. We report our results in two different scenarios, marine ecology, and architectural heritage.

Human Centered AI Workshop at NeurIPS 2021 - Thirty-fifth Conference on Neural Information Processing Systems, Online event, 13/12/2021

Keywords

Computer Vision and Pattern Recognition, Artificial Intelligence, Human-Computer Interaction

CNR authors

Cignoni Paolo, Ponchio Federico, Corsini Massimiliano, Muntoni Alessandro, Pavoni Gaia

CNR institutes

ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"