← 研踪 PaperTrailarXiv:2611.16056 · v1
Sub-actions in Action: Text-Guided Hand-Role Alignment for Sub-Action Recognition
首次提交 2026-11-12 06:12
版本更新 2026-11-12 06:12
雷达观测 2026-11-13 14:16
摘要
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.
作者备注：Accepted at BMVC 2026, Supplementary material is available
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v1 · 2026-11-12 06:12 · 最新观测版本
Sub-actions in Action: Text-Guided Hand-Role Alignment for Sub-Action Recognition
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.
查看此版本解读 →查看此版本原文 ↗