Image saliency detection has recently witnessed rapid progress due to deep convolutional
neural networks. However, none of the existing methods is able to identify object instances …

Instance segmentation is the inevitable result of object detection and semantic segmentation
in developing from coarse-to fine-grained inference, which is a popular research field in …

The instance segmentation task is relatively difficult in computer vision, which requires not
only high-quality masks but also high-accuracy instance category classification. Mask R …

Salient instance segmentation (SIS) can be considered as the next generation task for the
saliency detection community. Most of the existing state-of-the-art methods used for this …

Salient instance segmentation (SIS) is an emerging field that evolves from salient object
detection (SOD), aiming at identifying individual salient instances using segmentation maps …

The significant amount of manual efforts in annotating pixel-level labels has triggered the
advancement of unsupervised saliency learning. However without supervision signals state …

The mechanism of human vision can easily detect and segment objects based on boundary
information. Even though the deep learning instance segmentation model based on …

Different from conventional instance segmentation, salient instance segmentation (SIS)
faces two difficulties. The first is that it involves segmenting salient instances only while …

Instance segmentation has made great progress in recent years. However, current
mainstream detection-based methods ignore the process of distinguishing different …

Segment Anything Model (SAM) has demonstrated powerful zero-shot segmentation
performance in natural scenes. The recently released Segment Anything Model 2 (SAM2) …