LAKE identifies sparse anomaly-sensitive neurons in pre-trained VLMs using minimal normal samples to build compact normality representations and achieve SOTA anomaly detection with neuron-level interpretability.
Aa-clip: Enhancing zero-shot anomaly detection via anomaly-aware clip
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
EV-CLIP introduces mask and context visual prompts to adapt CLIP for improved few-shot video action recognition under visual challenges such as low light and egocentric views, outperforming other efficient methods with backbone-scale-independent efficiency.
citing papers explorer
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Latent Anomaly Knowledge Excavation: Unveiling Sparse Sensitive Neurons in Vision-Language Models
LAKE identifies sparse anomaly-sensitive neurons in pre-trained VLMs using minimal normal samples to build compact normality representations and achieve SOTA anomaly detection with neuron-level interpretability.
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EV-CLIP: Efficient Visual Prompt Adaptation for CLIP in Few-shot Action Recognition under Visual Challenges
EV-CLIP introduces mask and context visual prompts to adapt CLIP for improved few-shot video action recognition under visual challenges such as low light and egocentric views, outperforming other efficient methods with backbone-scale-independent efficiency.