RGB-D salient object detection: A survey
Salient object detection, which simulates human visual perception in locating the most
significant object (s) in a scene, has been widely applied to various computer vision tasks …
significant object (s) in a scene, has been widely applied to various computer vision tasks …
Salient object detection: A survey
Detecting and segmenting salient objects from natural scenes, often referred to as salient
object detection, has attracted great interest in computer vision. While many models have …
object detection, has attracted great interest in computer vision. While many models have …
Segment anything
Abstract We introduce the Segment Anything (SA) project: a new task, model, and dataset for
image segmentation. Using our efficient model in a data collection loop, we built the largest …
image segmentation. Using our efficient model in a data collection loop, we built the largest …
Camouflaged object detection with feature decomposition and edge reconstruction
Camouflaged object detection (COD) aims to address the tough issue of identifying
camouflaged objects visually blended into the surrounding backgrounds. COD is a …
camouflaged objects visually blended into the surrounding backgrounds. COD is a …
Zoom in and out: A mixed-scale triplet network for camouflaged object detection
Visual saliency transformer
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 …
architectures. Alternatively, we rethink this task from a convolution-free sequence-to …
Polyp-pvt: Polyp segmentation with pyramid vision transformers
Most polyp segmentation methods use CNNs as their backbone, leading to two key issues
when exchanging information between the encoder and decoder: 1) taking into account the …
when exchanging information between the encoder and decoder: 1) taking into account the …
SwinNet: Swin transformer drives edge-aware RGB-D and RGB-T salient object detection
Convolutional neural networks (CNNs) are good at extracting contexture features within
certain receptive fields, while transformers can model the global long-range dependency …
certain receptive fields, while transformers can model the global long-range dependency …
Detecting camouflaged object in frequency domain
Camouflaged object detection (COD) aims to identify objects that are perfectly embedded in
their environment, which has various downstream applications in fields such as medicine …
their environment, which has various downstream applications in fields such as medicine …