Detection and localization of regions of images that attract immediate human visual attention
is currently an intensive area of research in computer vision. The capability of automatic …

Although remarkable progress has been made in salient object detection (SOD) in natural
scene images (NSI), the SOD of optical remote sensing images (RSI) still faces significant …

Abstract Recently, Meta AI Research approaches a general, promptable segment anything
model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B) …

The recently proposed camouflaged object detection (COD) attempts to segment objects that
are visually blended into their surroundings, which is extremely complex and difficult in real …

Existing state-of-the-art saliency detection methods heavily rely on CNN-based
architectures. Alternatively, we rethink this task from a convolution-free sequence-to …

Vision transformers have recently shown strong global context modeling capabilities in
camouflaged object detection. However, they suffer from two major limitations: less effective …

Spotting objects that are visually adapted to their surroundings is challenging for both
humans and AI. Conventional generic/salient object detection techniques are suboptimal for …

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 …

Although current salient object detection (SOD) works have achieved significant progress,
they are limited when it comes to the integrity of the predicted salient regions. We define the …

Automatically detecting/segmenting object (s) that blend in with their surroundings is difficult
for current models. A major challenge is that the intrinsic similarities between such …