Few-Shot Transfer Learning for Saliency Prediction
1 papers with code • 4 benchmarks • 1 datasets
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
These leaderboards are used to track progress in Few-Shot Transfer Learning for Saliency Prediction
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SALICON
Most implemented papers
$n$-Reference Transfer Learning for Saliency Prediction
The proposed framework is gradient-based and model-agnostic.
