Camouflaged object detection (COD) and salient object detection (SOD) are two distinct yet
closely-related computer vision tasks widely studied during the past decades. Though …

This paper explores the potential of using the SAM (Segment-Anything Model) segmentator
to enhance the segmentation capability of known methods. SAM is a promptable …

Social media is increasingly plagued by realistic fake images, making it hard to trust content.
Previous algorithms to detect these fakes often fail in new, real-world scenarios because …

Despite the growing prevalence of black-box pre-trained models (PTMs) such as prediction
API services, there remains a significant challenge in directly applying general models to …

Camouflaged Object Segmentation (COS) faces significant challenges due to the scarcity of
annotated data, where meticulous pixel-level annotation is both labor-intensive and costly …

With the wide application of deep learning in image processing, salient object detection
(SOD) in underwater sonar images has become an important research topic. However, due …

Salient object detection (SOD) aims at finding the most salient objects in images and outputs
pixel-level binary masks. Transformer-based methods achieve promising performance due …

Semantic segmentation in adverse weather scenarios is a critical task for autonomous
driving systems. While foundation models have shown promise, the need for specialized …