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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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.