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A robust anomaly detection mechanism should possess the capability to effectively remediate anomalies, restoring them to a healthy state, while preserving essential healthy information. Despite the efficacy of existing generative models in learning the und ...
Precisely localizing anomalies in medical images remains a significant challenge due to their heterogeneous nature across modalities and organs. While initial efforts excelled in identifying prominent anomalies, detecting minute target lesions posed signif ...
Zero-shot classification capabilities naturally arise in models trained within a vision-language contrastive framework. Despite their classification prowess, these models struggle in dense tasks like zero-shot open-vocabulary segmentation. This deficiency ...
International Conference on Learning Representations, ICLR2025
