Cross-modal novel class discovery for ATR: a JEPA approach (Invited Paper)

Automatic Target Recognition (ATR) in tactical and remote sensing scenarios frequently encounters novel target classes absent from labeled training data, while multimodal sensors (EO, SAR, IR, radar) offer complementary cues for robust detection. Conventional unimodal or supervised methods struggle to discover and categorize these unseen targets in unlabeled streams. We propose a cross-modal novel class discovery framework leveraging a JEPA-based approach to learn independent embeddings across multiple modalities, enabling label-free identification of novel targets. Modality-specific encoders generate parallel embeddings and project them into latent space. Novel classes are discovered through sustained energy spikes in unimodal temporal prediction and spikes in cross-modal prediction. The framework provides novel class discovery resilient to sensor noise, occlusion, and deception. This work advances scalable, adaptive ATR for dynamic environments with emerging threats.