Teaching robots to see clearly: optimizing cross-modal domain adaptation models through sequential classification and user evaluation

Jayant Singh; Lalit Bauskar; Kara Capps; Ori Yonay; Alicia Yong; Seth Polsley; Samantha Ray; Suya You; Tracy Hammond

doi:10.1117/12.2618853

27 May 2022 Teaching robots to see clearly: optimizing cross-modal domain adaptation models through sequential classification and user evaluation

Jayant Singh, Lalit Bauskar, Kara Capps, Ori Yonay, Alicia Yong, Seth Polsley, Samantha Ray, Suya You, Tracy Hammond

Author Affiliations +

Proceedings Volume 12100, Multimodal Image Exploitation and Learning 2022; 121000J (2022) https://doi.org/10.1117/12.2618853
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Conference Poster

Abstract

As society becomes increasingly reliant on autonomous vehicles, it becomes necessary for these vehicles to have the ability to navigate new environments. Environmental data is expensive to label especially because it comes from many different sensors, and it can be difficult to interpret how the underlying models works. Therefore, an adequate machine learning model for multi-modal, unsupervised domain adaptation (UDA) that is accurate and explainable is necessary. We aim to improve xMUDA, a state-of-the-art multi-modal UDA model by incorporating a multi-step binary classification algorithm, which allows us to prioritize certain data labels, and alongside human evaluation, we report the mIoU and accuracy of the final output.

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Jayant Singh, Lalit Bauskar, Kara Capps, Ori Yonay, Alicia Yong, Seth Polsley, Samantha Ray, Suya You, and Tracy Hammond "Teaching robots to see clearly: optimizing cross-modal domain adaptation models through sequential classification and user evaluation", Proc. SPIE 12100, Multimodal Image Exploitation and Learning 2022, 121000J (27 May 2022); https://doi.org/10.1117/12.2618853

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