B-ACT improves label efficiency in temporal action segmentation by selecting only boundary frames for annotation via a two-stage uncertainty-driven process that fuses neighborhood uncertainty, class ambiguity, and temporal dynamics.
Actionformer: Localizing moments of actions with transformers,
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cs.CV 2years
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A new adapter module combining boundary-aware state space modeling with spatial processing boosts localization and robustness in temporal action detection.
citing papers explorer
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Boundary-Centric Active Learning for Temporal Action Segmentation
B-ACT improves label efficiency in temporal action segmentation by selecting only boundary frames for annotation via a two-stage uncertainty-driven process that fuses neighborhood uncertainty, class ambiguity, and temporal dynamics.
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Efficient Spatial-Temporal Focal Adapter with SSM for Temporal Action Detection
A new adapter module combining boundary-aware state space modeling with spatial processing boosts localization and robustness in temporal action detection.