Lag-aware cross-hand alignment for dual-hand action segmentation
This paper introduces Lag-Aware Cross-Hand Alignment (LACA), a lightweight module that explicitly estimates directional temporal-offset distributions between hand-specific feature streams. LACA improves dual-hand action segmentation performance on HA-ViD and ATTACH datasets with minimal parameter increase. A future-free variant LACA-C enables real-time perception.
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[Submitted on 28 Jul 2026]
Title:Lag-aware cross-hand alignment for dual-hand action segmentation
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Abstract:Dual-hand action segmentation commonly fuses left- and right-hand representations at identical temporal indices, although coordinated hand transitions may occur with nonzero and time-varying delays. We introduce Lag-Aware Cross-Hand Alignment (LACA), a lightweight module that explicitly estimates directional temporal-offset distributions between hand-specific feature streams. LACA retrieves cross-hand information from the estimated offsets and incorporates a learned null state to suppress transfer when no compatible cross-hand transition is supported. Alignment is supervised using compatibility-aware targets derived automatically from frame-level training annotations, without requiring additional labels. Analysis of the HA-ViD and ATTACH training annotations reveals robust nonzero cross-hand matches for 44.7% and 48.9% of transition anchors, respectively, compared with 18.6% and 21.3% under temporally shifted controls. When integrated into Polyphony, LACA improves the two-hand mean F1@50 from 40.4 to 42.5 and boundary F1 from 56.5 to 59.6 on HA-ViD, and from 19.9 to 21.8 and 44.7 to 47.9, respectively, on ATTACH, relative to our reproduced Polyphony baseline. These gains require only approximately 0.0086 million additional trainable parameters. We further introduce LACA-C, a future-free variant that restricts alignment and the complete inference pipeline to current and past observations. On ATTACH, LACA-C achieves 83.6% transition-cue recall, a seed-averaged median availability delay of 233~ms, 0.72 false cues per minute, and segmentation-stage throughput of 224.9 current-position predictions per second. These results demonstrate that explicit cross-hand temporal alignment improves both action segmentation and boundary localization while supporting timely future-free perception.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.26215 [cs.CV]
(or arXiv:2607.26215v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.26215
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Fatemeh Ziaeetabar [view email] [v1] Tue, 28 Jul 2026 19:31:45 UTC (2,267 KB)
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