LiquidTAD distills liquid neural dynamics into a vectorized parallel temporal operator and hierarchical decay sharing to achieve efficient action detection with substantially reduced model size and computation.
Temporalmaxer: Maximize temporal context with only max pooling for temporal action localization
3 Pith papers cite this work. Polarity classification is still indexing.
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Gaze-following models on extended 4D-OR and Team-OR datasets reach F1 scores of 0.92 for clinical role prediction and 0.95 for surgical phase recognition while improving team communication detection by over 30%.
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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LiquidTAD: Efficient Temporal Action Detection via Parallel Liquid-Inspired Temporal Relaxation
LiquidTAD distills liquid neural dynamics into a vectorized parallel temporal operator and hierarchical decay sharing to achieve efficient action detection with substantially reduced model size and computation.
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Where are they looking in the operating room?
Gaze-following models on extended 4D-OR and Team-OR datasets reach F1 scores of 0.92 for clinical role prediction and 0.95 for surgical phase recognition while improving team communication detection by over 30%.
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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.