Recent progress on salient object detection (SOD) mainly benefits from multi-scale learning,
where the high-level and low-level features collaborate in locating salient objects and …

RGB-D salient object detection (SOD) aims to segment the most attractive objects in a pair of
cross-modal RGB and depth images. Currently, most existing RGB-D SOD methods focus on …

Camouflaged object detection (COD) aims to discover objects that blend in with the
background due to similar colors or textures, etc. Existing deep learning methods do not …

Recently, fully convolutional networks (FCNs) have made great progress in the task of
salient object detection and existing state-of-the-arts methods mainly focus on how to …

Contour information plays a vital role in salient object detection. However, excessive false
positives remain in predictions from existing contour-based models due to insufficient …

Deep clustering aims to promote clustering tasks by combining deep learning and clustering
together to learn the clustering-oriented representation, and many approaches have shown …

Camouflaged object segmentation (COS) is a very challenging task due to the deceitful
appearances of the candidate objects to the noisy backgrounds. Most existing state-of-the …

In this paper, we identify and address a serious design bias of existing salient object
detection (SOD) datasets, which unrealistically assume that each image should contain at …

Convolutional Neural Network (CNN) has shown their advantages in salient object
detection. CNN can generate great saliency maps because it can obtain high-level semantic …

Much of the recent efforts on salient object detection (SOD) have been devoted to producing
accurate saliency maps without being aware of their instance labels. To this end, we …