Spike-NVPT creates noise-robust binary visual prompts by using integrate-and-fire spiking neurons to filter signals and discretize them, yielding up to 11.2% better robustness than standard prompt tuning while keeping clean accuracy competitive.
Automated flower classification over a large number of classes
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A3B2 introduces an adaptive asymmetric adapter with uncertainty-aware dampening to reduce branch bias in few-shot vision-language image classification and outperforms standard adapter and prompt methods.
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Spike-NVPT: Learning Robust Visual Prompts via Bio-Inspired Temporal Filtering and Discretization
Spike-NVPT creates noise-robust binary visual prompts by using integrate-and-fire spiking neurons to filter signals and discretize them, yielding up to 11.2% better robustness than standard prompt tuning while keeping clean accuracy competitive.
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A$_3$B$_2$: Adaptive Asymmetric Adapter for Alleviating Branch Bias in Vision-Language Image Classification with Few-Shot Learning
A3B2 introduces an adaptive asymmetric adapter with uncertainty-aware dampening to reduce branch bias in few-shot vision-language image classification and outperforms standard adapter and prompt methods.
- GRPO-TTA: Test-Time Visual Tuning for Vision-Language Models via GRPO-Driven Reinforcement Learning