サブアクション認識のためのテキスト誘導による左右手役割アライメント

原文アブストラクト

Our objective is to enhance VLMs ability to distinguish the roles of the left and right hands in egocentric actions. We introduce sub-action detection, a task that requires models to explicitly recognise the left- and right-hand roles and their manipulated objects within an ongoing high-level action. To address this challenge, we propose BiScopeNet, a framework that augments existing video-LLMs without modifying their vision encoders or language models. BiScopeNet incorporates two components: FocusAlign, a gated text-guided attention-based module that selectively aligns video features with textual context to emphasise action-relevant cues, and MetaLoss, a masked, object-weighted loss that prioritises visually grounded terms. To supervise sub-action detection, we introduce BiScopeCap-100K, a large-scale video-level dataset curated from existing egocentric datasets, providing dense captions that jointly describe high-level actions, left/right-hand roles, and manipulated objects, enriched with spatial metadata for grounding. Across the evaluated Video-LLM backbones, BiScopeNet improves action, hand-role, and object recognition over the corresponding supervised baselines, achieving the strongest aggregate performance among the evaluated configurations on BiScopeCap.