Focusing on the issue of how to effectively capture and utilize cross-modality information in
RGB-D salient object detection (SOD) task, we present a convolutional neural network …

RGB-T salient object detection (SOD) aims at utilizing the complementary cues of RGB and
Thermal (T) modalities to detect and segment the common objects. However, on one hand …

In this work, we propose a novel depth-induced multi-scale recurrent attention network for
RGB-D saliency detection, named as DMRA. It achieves dramatic performance especially in …

With the supplement of texture and geometry cues in depth maps, salient object detection
(SOD) shifts from 2D to 3D, aiming to detect the most attractive object in a pair of color and …

Most existing RGB-D salient detection models pay more attention to the quality of the depth
images, while in some special cases, the quality of RGB images may even have greater …

Conventional RGB-D salient object detection methods aim to leverage depth as
complementary information to find the salient regions in both modalities. However, the …

Low rank and sparse representation (LRSR) with dual-dictionaries-based methods for
detecting anomalies in hyperspectral images (HSIs) are proven to be effective. However, the …

The RGB-D salient object detection algorithm simulates human attention behavior and
attempts to locate the most visually prominent object (s) from a set of RGB and depth …

Aerial image target detection is essential for urban planning, traffic monitoring, and disaster
assessment. However, existing detection algorithms struggle with small target recognition …

Cross-modal complementary information from RGB images and depth maps has brought
new vitality and progress into salient object detection (SOD). Most existing RGB-D SOD …