Beyond Low-Rank: Low-Rank Sparse Prompting via Spiking Neural Network and Prompt Factorization
Researchers have developed a novel framework called LoRSP, which integrates brain-inspired spiking neural networks with low-rank factorization for visual prompting. This approach generates sparse, instance-specific prompts for adapting vision models, aiming to improve efficiency and generalization compared to dense pixel-level prompts. Experiments show LoRSP achieves competitive performance with fewer tunable parameters across various vision backbones. AI
IMPACT This research could lead to more efficient and adaptable vision models by reducing computational overhead and improving generalization.