← 研踪 PaperTrailCVF · CVPR 2026:cvf:e5d8979b60a64eb807a459dce0f5d48f · 记录 #1
DETACH : Decomposed Spatio-Temporal Alignment for Exocentric Video and Ambient Sensors with Staged Learning
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 03:01
摘要
Aligning egocentric video with wearable sensors has shown promise for human action recognition, but faces practical limitations in user discomfort, privacy concerns, and scalability. We explore exocentric video with ambient sensors as a non-intrusive, scalable alternative. However, the Global Alignment approach prevalent in egocentric-wearable settings fails in this new setting due to two problems: (P1) inability to capture local details such as subtle motions, and (P2) over-reliance on modality-invariant temporal features that distort negative relationships. To resolve these problems, we propose DETACH, a decomposed spatio-temporal alignment framework. By decomposing both modalities into spatial-temporal components, we preserve subtle temporal cues in videos from spatial features, and convert implicit sensor channel activations into explicit spatial prototypes via online clustering, thereby establishing cross-modal spatial grounding. To avoid over-reliance on temporal features, a spatial-temporal weighted contrastive loss leverages this grounding for fine-grained temporal alignment, prioritizing hard negatives and suppressing false negatives. Comprehensive experiments with downstream tasks on Opportunity++ and HWU-USP datasets demonstrate improvements of up to 30% in F1 and 50% in mAP over adapted egocentric-wearable baselines.
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记录 #1 · 2026 · 最新观测版本
DETACH : Decomposed Spatio-Temporal Alignment for Exocentric Video and Ambient Sensors with Staged Learning
Aligning egocentric video with wearable sensors has shown promise for human action recognition, but faces practical limitations in user discomfort, privacy concerns, and scalability. We explore exocentric video with ambient sensors as a non-intrusive, scalable alternative. However, the Global Alignment approach prevalent in egocentric-wearable settings fails in this new setting due to two problems: (P1) inability to capture local details such as subtle motions, and (P2) over-reliance on modality-invariant temporal features that distort negative relationships. To resolve these problems, we propose DETACH, a decomposed spatio-temporal alignment framework. By decomposing both modalities into spatial-temporal components, we preserve subtle temporal cues in videos from spatial features, and convert implicit sensor channel activations into explicit spatial prototypes via online clustering, thereby establishing cross-modal spatial grounding. To avoid over-reliance on temporal features, a spatial-temporal weighted contrastive loss leverages this grounding for fine-grained temporal alignment, prioritizing hard negatives and suppressing false negatives. Comprehensive experiments with downstream tasks on Opportunity++ and HWU-USP datasets demonstrate improvements of up to 30% in F1 and 50% in mAP over adapted egocentric-wearable baselines.
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