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 …
Steel surface defect recognition: A survey
Steel surface defect recognition is an important part of industrial product surface defect
detection, which has attracted more and more attention in recent years. In the development …
detection, which has attracted more and more attention in recent years. In the development …
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 …
Kubric: A scalable dataset generator
Data is the driving force of machine learning, with the amount and quality of training data
often being more important for the performance of a system than architecture and training …
often being more important for the performance of a system than architecture and training …
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 …