Continual Multimodal Egocentric Activity Recognition via Modality-Aware Novel Detection

ArXi:2603.16970v1 Announce Type: new Multimodal egocentric activity recognition integrates visual and inertial cues for robust first-person behavior understanding. However, deploying such systems in open-world environments requires detecting novel activities while continuously learning from non-stationary streams. Existing methods rely on the main logits for novelty scoring, without fully exploiting the complementary evidence available from individual modalities.