  Saliency prediction has made great strides over the past two decades, with
current techniques modeling low-level information, such as color, intensity and
size contrasts, and high-level one, such as attention and gaze direction for
entire objects. Despite this, these methods fail to account for the
dissimilarity between objects, which humans naturally do. In this paper, we
introduce a detection-guided saliency prediction network that explicitly models
the differences between multiple objects, such as their appearance and size
dissimilarities. Our approach is general, allowing us to fuse our object
dissimilarities with features extracted by any deep saliency prediction
network. As evidenced by our experiments, this consistently boosts the accuracy
of the baseline networks, enabling us to outperform the state-of-the-art models
on three saliency benchmarks, namely SALICON, MIT300 and CAT2000.
