MAND: Modality-Aware Novelty Detection for Open-World Egocentric Activity Recognition
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 data streams. Existing methods rely on the main fused logits for novelty scoring, without fully exploiting the complementary evidence available from individual modalities. Because these logits are often dominated by RGB, cues from other modalities, particularly IMU, remain underutilized, and this imbalance worsens as catastrophic forgetting accumulates. To address this, we propose MAND, a modality-aware framework for multim...
| Authors | Hyejeong Im, Wonseon Lim, Dae-Won Kim |
|---|---|
| Primary category | cs.CV |
| Published | 17 March 2026 |
| Last updated at source | 14 June 2026 |
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