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 …

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 …

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 …

Convolutional neural networks (CNNs) are good at extracting contexture features within
certain receptive fields, while transformers can model the global long-range dependency …

Feature fusion, the combination of features from different layers or branches, is an
omnipresent part of modern network architectures. It is often implemented via simple …

Sensor techniques and emerging CNN models have greatly facilitated the development of
collaborative fault diagnosis. Existing CNN models apply different fusion schemes to …

Focusing on the issue of how to effectively capture and utilize cross-modality information in
RGB-D salient object detection (SOD) task, we present a convolutional neural network …

Colonoscopy is an effective technique for detecting colorectal polyps, which are highly
related to colorectal cancer. In clinical practice, segmenting polyps from colonoscopy …

Complex backgrounds and similar appearances between objects and their surroundings are
generally recognized as challenging scenarios in Salient Object Detection (SOD). This …

We present a comprehensive study on a new task named camouflaged object detection
(COD), which aims to identify objects that are" seamlessly" embedded in their surroundings …