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.
Where culture fades: Revealing the cultural gap in text-to-image generation
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PhenoYieldNet introduces a crop-aware temporal decoder with a Crop Phenology Bank and Crop Phenology Attention module, plus self-supervised adaptation of a pre-trained encoder, to improve multi-crop yield prediction.
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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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PhenoYieldNet: Learning Crop-Aware Phenological Responses for Multi-Crop Yield Prediction
PhenoYieldNet introduces a crop-aware temporal decoder with a Crop Phenology Bank and Crop Phenology Attention module, plus self-supervised adaptation of a pre-trained encoder, to improve multi-crop yield prediction.