

DA CLIP
Overview :
DA-CLIP is a degradation-aware visual language model that can serve as a general framework for image recovery. It trains an additional controller to enable a fixed CLIP image encoder to predict high-quality feature embeddings, which are then integrated into the image recovery network, leading to high-fidelity image reconstruction. The controller also outputs degradation features that match the true corruption of the input, providing a natural classifier for different degradation types. DA-CLIP is further trained on mixed degradation datasets, improving performance on specific degradation and unified image recovery tasks.
Target Users :
DA-CLIP can be used for image recovery tasks, especially in handling corrupted inputs, it can improve prediction accuracy.
Use Cases
Use DA-CLIP to recover corrupted digital images
Use DA-CLIP to recover corrupted natural images
Use DA-CLIP to recover corrupted medical images
Features
Trains an additional controller to enable a fixed CLIP image encoder to predict high-quality feature embeddings
Integrates feature embeddings into the image recovery network to learn high-fidelity image reconstruction
Outputs degradation features that match the true corruption of the input, providing a natural classifier for different degradation types
Trained on mixed degradation datasets, improving performance on specific degradation and unified image recovery tasks
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