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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cs.CV 3years
2026 3verdicts
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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.
GRPO-TTA reformulates test-time adaptation for vision-language models as group-wise policy optimization via top-K sampling from CLIP distributions and alignment/dispersion rewards to tune the visual encoder.
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