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.
데이터셋
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The SALIency in CONtext (SALICON) dataset contains 10,000 training images, 5,000 validation images and 5,000 test images for saliency prediction. This dataset has been created by annotating saliency in images from MS COCO. The ground-truth saliency annotations include fixations generated from mouse trajectories. To improve the data quality, isolated fixations with low local density have been excluded. The training and validation sets, provided with ground truth, contain the following data fields: image, resolution and gaze. The testing data contains only the image and resolution fields. Source: DeepFix: A Fully Convolutional Neural Network for predicting Human Eye Fixations Image Source: http://salicon.net/explore/