$n$-Reference Transfer Learning for Saliency Prediction

9 Jul 2020 Yan Luo Yongkang Wong Mohan S. Kankanhalli Qi Zhao

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... (read more)

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Datasets


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Few-Shot Transfer Learning for Saliency Prediction SALICON->WebpageSaliency - 10-shot DINet+FT|Ref NSS 1.6439 # 1
AUC 0.8276 # 1
CC 0.6605 # 1
Few-Shot Transfer Learning for Saliency Prediction SALICON->WebpageSaliency - 1-shot DINet+FT|Ref NSS 1.5077 # 1
AUC 0.8051 # 1
CC 0.6121 # 1
Few-Shot Transfer Learning for Saliency Prediction SALICON->WebpageSaliency - 1-shot ResNet+FT|Ref NSS 1.4272 # 2
AUC 0.7983 # 2
CC 0.5817 # 2
Few-Shot Transfer Learning for Saliency Prediction SALICON->WebpageSaliency - 5-shot DINet+FT|Ref NSS 1.6085 # 1
AUC 0.8200 # 1
CC 0.6468 # 1
Few-Shot Transfer Learning for Saliency Prediction SALICON->WebpageSaliency - EUB DINet+FT|Ref NSS 1.8831 # 1
AUC 0.8494 # 1
CC 0.7442 # 1

Methods used in the Paper


METHOD TYPE
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