  In this paper, we propose several novel deep learning methods for object
saliency detection based on the powerful convolutional neural networks. In our
approach, we use a gradient descent method to iteratively modify an input image
based on the pixel-wise gradients to reduce a cost function measuring the
class-specific objectness of the image. The pixel-wise gradients can be
efficiently computed using the back-propagation algorithm. The discrepancy
between the modified image and the original one may be used as a saliency map
for the image. Moreover, we have further proposed several new training methods
to learn saliency-specific convolutional nets for object saliency detection, in
order to leverage the available pixel-wise segmentation information. Our
methods are extremely computationally efficient (processing 20-40 images per
second in one GPU). In this work, we use the computed saliency maps for image
segmentation. Experimental results on two benchmark tasks, namely Microsoft
COCO and Pascal VOC 2012, have shown that our proposed methods can generate
high-quality salience maps, clearly outperforming many existing methods. In
particular, our approaches excel in handling many difficult images, which
contain complex background, highly-variable salient objects, multiple objects,
and/or very small salient objects.
