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Spiking neural networks (SNNs) offer sparse and event-driven computation, making them attractive for energy-constrained reinforcement learning (RL) on edge devices. In value-based RL, deep spiking Q-networks (DSQNs) combine such efficiency with action-value estimation for decision making. However, existing DSQNs often...
PointPiT is proposed, a partition-invariant tuning framework for scene-level point clouds that integrates local geometric patterns with global scene context to mitigate partition-induced representation shifts, and achieves consistent state-of-the-art performance among representative PEFT methods.
MoSaiC, a novel Motion-Saliency Complementary masked modeling framework for self-supervised point cloud video representation learning, couples three components: Curriculum Motion-Saliency Masking (CMSM), which guides the masking process toward motion-salient tokens under a curriculum schedule; Normal-Flow Motion (NFM)...
SpikingMOT is proposed as a spike-driven tracker that adaptively models sparse trajectory dynamics with spiking neural networks (SNNs) and brings SNNs into MOT, opening a promising direction for efficient tracking.
This work proposes a simple yet effective framework called Anchor-guided adaptive inter-frame motion cues propagating (Again-Pose), reformulating pose estimation in degraded frames as a motion-guided recovery task, significantly outperforms state-of-the-art methods in robustness and stability.
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