Light field salient object detection (LF SOD) has recently received increasing attention.
However, most current works typically rely on an individual focal stack backbone for feature …

While significant progress has been made in recent years in the field of salient object
detection, there are still limitations in heterogeneous modality fusion and salient feature …

In recent years, excellent RGB-D salient object detection performance has been achieved.
However, existing detection methods generally require a large number of model parameters …

Most existing RGB-D salient object detection methods utilize the convolutional neural
networks (CNNs) to extract features. However, they fail to extract global information due to …

Currently, the majority of RGB-Depth salient object detection (SOD) methods utilize the
encoder–decoder architecture. However, they often fail to utilize the encoding and decoding …

RGB-T salient object detection attempts to locate the most attractive target on RGB image
and corresponding thermal map. Due of the intrinsic disparity between RGB and thermal …

Existing RGB-D saliency detection models have not fully considered the differences
between features at various levels, and lack an effective mechanism for cross-level feature …

Salient object detection (SOD) aims to identify the most salient regions in an image and is an
important process for a variety of computer vision tasks today. Most of the existing RGB-D …

The purpose of RGB-D Salient Object Detection (SOD) is to pinpoint the most visually
conspicuous areas within images accurately. Numerous conventional models heavily rely …

Existing RGB-D salient object detection (SOD) models have large numbers of parameters,
high computational complexity, and slow inference speeds, limiting their deployment on …