Abstract
The categories and appearance of salient objects vary
from image to image, therefore, saliency detection is an
image-specific task. Due to lack of large-scale saliency
training data, using deep neural networks (DNNs) with pretraining is difficult to precisely capture the image-specific
saliency cues. To solve this issue, we formulate a zero-shot
learning problem to promote existing saliency detectors.
Concretely, a DNN is trained as an embedding function
to map pixels and the attributes of the salient/background
regions of an image into the same metric space, in which
an image-specific classifier is learned to classify the pixels. Since the image-specific task is performed by the classifier, the DNN embedding effectively plays the role of a
general feature extractor. Compared with transferring the
learning to a new recognition task using limited data, this
formulation makes the DNN learn more effectively from
small data. Extensive experiments on five data sets show
that our method significantly improves accuracy of existing methods and compares favorably against state-of-theart approaches