Invited panel discussion: joint data learning

Lynne L. Grewe; Chee-Yee Chong; Ivan Kadar

doi:10.1117/12.2598863

14 April 2021 Invited panel discussion: joint data learning

Lynne L. Grewe, Chee-Yee Chong, Ivan Kadar

Proceedings Volume 11756, Signal Processing, Sensor/Information Fusion, and Target Recognition XXX; 1175602 (2021) https://doi.org/10.1117/12.2598863
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

AI techniques are based on learning a model based on a large available data set. The data sets typically are from a single modality (e.g., imagery) and hence the model is based on a single modality. Likewise, multiple models are each built for a common scenario (e.g., video and natural language processing of text describing the situation). There are issues of robustness, efficiency, and explainability needed. A second modality can improve efficiency (e.g., cueing), robustness (e.g., results can not be fooled such as adversary systems), and explainability from different sources help. The challenge is how to organize the data needed for joint data training and model building. For example, what is needed (1) structure for indexing data as an object file, (2) recording of metadata for effective correlation, and (3) supporting models and analysis for model interpretability for users. There are a variety of questions to be discussed, explored, and analyzed for fusion-based AI tool.

Conference Presentation

Citation Download Citation

Lynne L. Grewe, Chee-Yee Chong, and Ivan Kadar "Invited panel discussion: joint data learning", Proc. SPIE 11756, Signal Processing, Sensor/Information Fusion, and Target Recognition XXX, 1175602 (14 April 2021); https://doi.org/10.1117/12.2598863

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