Few-Shot Transfer Learning for Saliency Prediction
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.
Papers
Showing 1–1 of 1 papers
| Title | Status | Hype |
|---|---|---|
| n-Reference Transfer Learning for Saliency Prediction | Code | 1 |
All datasetsSALICON->WebpageSaliency - 1-shotSALICON->WebpageSaliency - 10-shotSALICON->WebpageSaliency - 5-shotSALICON->WebpageSaliency - EUB
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | DINet+FT|Ref | NSS | 1.61 | — | Unverified |