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City-scale audio-visual reasoning is currently dominated by expensive agentic pipelines, not standalone multimodal models.
Models trained with hand-object masked strategies can achieve superior HOI recognition by effectively isolating and utilizing hand- and object-centric cues.
Whareformer outperforms prior models in tracking occluded objects in egocentric videos, achieving state-of-the-art results with minimal training data.
