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

Recently, the vision transformer has achieved great success by pushing the state-of-the-art
of various vision tasks. One of the most challenging problems in the vision transformer is that …

Recently, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in
over 200 countries, influencing billions of humans. To control the infection, identifying and …

Salient object detection is the pixel-level dense prediction task which can highlight the
prominent object in the scene. Recently U-Net framework is widely used, and continuous …

Recently, relying on convolutional neural networks (CNNs), many methods for salient object
detection in optical remote-sensing images (ORSI-SOD) are proposed. However, most …

The ability to deal with intra and inter-modality features has been critical to the development
of RGB-D salient object detection. While many works have advanced in leaps and bounds in …

Recent progress on salient object detection (SOD) mainly benefits from multi-scale learning,
where the high-level and low-level features collaborate in locating salient objects and …

RGB-T salient object detection (SOD) aims at utilizing the complementary cues of RGB and
Thermal (T) modalities to detect and segment the common objects. However, on one hand …

Depth cues are known to be useful for visual perception. However, direct measurement of
depth is often impracticable. Fortunately, though, modern learning-based methods offer …

Convolutional neural network (CNN)-based fault detection approaches based on
multisource signals have attracted increasing interest from the research community and …