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SotA/Computer Vision/Few-Shot Transfer Learning for Saliency Prediction

Few-Shot Transfer Learning for Saliency Prediction

27 benchmarks1 papers

Saliency prediction aims to predict important locations in a visual scene. It is a per-pixel regression task with predicted values ranging from 0 to 1.

Benefiting from deep learning research and large-scale datasets, saliency prediction has achieved significant success in the past decade. However, it still remains challenging to predict saliency maps on images in new domains that lack sufficient data for data-hungry models.

Benchmarks

Few-Shot Transfer Learning for Saliency Prediction on SALECI

KL

Few-Shot Transfer Learning for Saliency Prediction on SALICON

AUCCCKLDSIMNSSsAUCIG

Few-Shot Transfer Learning for Saliency Prediction on MIT300

AUC-JuddCCKLDNSSSIMsAUC

Few-Shot Transfer Learning for Saliency Prediction on SALICON->WebpageSaliency - 1-shot

NSSAUCCC

Few-Shot Transfer Learning for Saliency Prediction on CAT2000

KL

Few-Shot Transfer Learning for Saliency Prediction on SALICON->WebpageSaliency - 10-shot

NSSAUCCC

Few-Shot Transfer Learning for Saliency Prediction on SALICON->WebpageSaliency - 5-shot

NSSAUCCC

Few-Shot Transfer Learning for Saliency Prediction on SALICON->WebpageSaliency - EUB

NSSAUCCC