In response to evolving complexities, automatic target recognition (ATR) is seamlessly transitioning into the realm of artificial intelligence (AI), embracing a future marked by innovation and adaptability. The traditional rule-based approaches are giving way to dynamic, data-driven methodologies empowered by AI. This shift is not merely a technological upgrade; it represents a strategic move to tackle the intricate challenges of contemporary defense. The integration of Transformer-based architectures in ATR reflects a fundamental departure from the limitations of predefined algorithms. This evolution is particularly notable in tasks requiring nuanced understanding, such as anomaly detection and trajectory prediction. Moreover, explainable AI (XAI) techniques are becoming integral, ensuring accuracy, transparency, and user trust in ATR systems. Looking forward, the trajectory of ATR is shaped by the promise of Generative Adversarial Networks (GANs) addressing data scarcity and Quantum Machine Learning optimizing high-dimensional analyses. Academic pursuits like federated learning and meta-learning provide the intellectual backbone for a future-ready ATR landscape. This narrative unfolds at the intersection of tradition and innovation, where the definition of ATR expands beyond its conventional boundaries, ushering in an era where AI becomes the linchpin in meeting the challenges of modern defense.

Moderators:
Asif Mehmood, Joint Artificial Intelligence Center (United States)
Matt Reismann, Bedrock Research (United States)

 

This panel is part of the Automatic Target Recognition conference.

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Event Details

FORMAT: Panel discussion followed by audience Q&A.
MENU: Coffee, decaf, and tea will be available in the exhibition area.
SETUP: Classroom and theater style seating.

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