Abstract: Zero-shot anomaly detection (ZSAD) requires detection models trained using auxiliary data to detect anomalies without any training sample in a target dataset. It is challenging since the ...
microCLIP is a lightweight self-training framework that adapts CLIP for fine-grained image classification without requiring labeled data. While CLIP is strong in zero-shot transfer, it primarily ...
No-Reference Image Quality Assessment (NR-IQA) focuses on designing methods to measure image quality in alignment with human perception when a high-quality reference image is unavailable. Most ...
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