Underwater detection poses significant challenges due to the unique characteristics of the
underwater environment, such as light attenuation, scattering, water turbidity, and the …

Recently, camouflaged object detection (COD), which suffers from numerous challenges
such as low contrast between camouflaged objects and background and large variations of …

In the computer vision community, great progresses have been achieved in salient object
detection from natural scene images (NSI-SOD); by contrast, salient object detection in …

The global and local contexts significantly contribute to the integrity of predictions in Salient
Object Detection (SOD). Unfortunately, existing methods still struggle to generate complete …

Salient Object Detection (SOD) is dominated by Encoder-Decoder networks which involve
multi-scale feature fusion and multi-resolution dense supervision. It is prevalent yet …

The purpose of co-salient object detection (CoSOD) is to detect the salient objects that co-
occur in a group of relevant images. CoSOD has been significantly prospered by recent …

Most existing RGB-D salient object detection (SOD) methods are primarily focusing on cross-
modal and cross-level saliency fusion, which has been proved to be efficient and effective …

Recently, benefitting from the rapid development of deep learning technology, the research
of salient object detection has achieved great progress. However, the performance of …

Most of the existing deep learning based salient object detection (SOD) models adopt multi-
level feature fusion strategies, and have achieved remarkable progress. However, current …

Recent research advances in salient object detection (SOD) could largely be attributed to
ever-stronger multi-scale feature representation empowered by the deep learning …