Multi-level feature fusion is a fundamental topic in computer vision for detecting, segmenting
and classifying objects at various scales. When multi-level features meet multi-modal cues …

Existing RGB-D Salient Object Detection (SOD) methods take advantage of depth cues to
improve the detection accuracy, while pay insufficient attention to the quality of depth …

With the availability of low-cost and compact 2.5/3D visual sensing devices, computer vision
community is experiencing a growing interest in visual scene understanding of indoor …

Salient object detection models mimic the behavior of human beings and capture the most
salient region/object from the images or scenes, this field contains many important …

The large availability of depth sensors provides valuable complementary information for
salient object detection (SOD) in RGBD images. However, due to the inherent difference …

Depth maps contain geometric clues for assisting Salient Object Detection (SOD). In this
paper, we propose a novel Cross-Modal Weighting (CMW) strategy to encourage …

Existing RGB-D saliency detection models do not explicitly encourage RGB and depth to
achieve effective multi-modal learning. In this paper, we introduce a novel multi-stage …

Depth data containing a preponderance of discriminative power in location have been
proven beneficial for accurate saliency prediction. However, RGB-D saliency detection …

The visual saliency detection model simulates the human visual system to perceive the
scene and has been widely used in many vision tasks. With the development of acquisition …

Salient object detection from RGB-D images aims to utilize both the depth view and RGB
view to automatically localize objects of human interest in the scene. Although a few earlier …