Zero shot domain adaptation for x-ray baggage scanning

Guangkun Li; Mark Horton; Joshua Soneson; Robert Schultz; Heather Hunter; David Barsic

doi:10.1117/12.2662920

13 June 2023 Zero shot domain adaptation for x-ray baggage scanning

Guangkun Li, Mark Horton, Joshua Soneson, Robert Schultz, Heather Hunter, David Barsic

Proceedings Volume 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications; 125290T (2023) https://doi.org/10.1117/12.2662920
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Conference Poster

Abstract

Deep learning based vision models have had significant success recently. However, one of the biggest challenges is to apply the established models to new targets and environments where no samples exist for training. To solve this zero-shot learning problem, we formulated a heterogeneous learning domain adaptation scenario where labeled data from other domains are available for the new target and for some other unrelated classes. We developed an innovative zero-shot domain adaptation (ZSDA) method by implementing end-to-end adversarial training with class-aware alignment and latent space feature transformation. We demonstrated the performance improvements in several applications in comparison with traditional unsupervised domain adaptation (UDA) approach including threat detection in X-ray security screening imagery.

Citation Download Citation

Guangkun Li, Mark Horton, Joshua Soneson, Robert Schultz, Heather Hunter, and David Barsic "Zero shot domain adaptation for x-ray baggage scanning", Proc. SPIE 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications, 125290T (13 June 2023); https://doi.org/10.1117/12.2662920

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