SVL pretraining enables SNNs to reach 85.4% top-1 accuracy on zero-shot 3D classification while outperforming prior SNNs on detection, segmentation, and action recognition with added open-world QA capability.
High-performance temporal reversible spiking neural networks withO(L)training memory andO(1)inference cost
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A fuzzy encoder-decoder architecture reduces information loss in spiking Q-learning and narrows the performance gap with conventional multi-modal networks on HighwayEnv driving tasks.
citing papers explorer
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SVL: Spike-based Vision-language Pretraining for Efficient 3D Open-world Understanding
SVL pretraining enables SNNs to reach 85.4% top-1 accuracy on zero-shot 3D classification while outperforming prior SNNs on detection, segmentation, and action recognition with added open-world QA capability.
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Fuzzy Encoding-Decoding to Improve Spiking Q-Learning Performance in Autonomous Driving
A fuzzy encoder-decoder architecture reduces information loss in spiking Q-learning and narrows the performance gap with conventional multi-modal networks on HighwayEnv driving tasks.