While significant progress has been made in recent years in the field of salient object
detection, there are still limitations in heterogeneous modality fusion and salient feature …

The discriminative correlation filter (DCF) is commonly used in aerial object tracking due to
its high tracking accuracy and computing speed. However, when similar object disturbances …

Most existing RGB-D salient object detection methods utilize the convolutional neural
networks (CNNs) to extract features. However, they fail to extract global information due to …

In object tracking, motion blur is a common challenge induced by rapid movement of target
object or long time exposure of the camera, which leads to poor tracking performance …

Recently, with the development of the Convolutional Neural Network and Vision
Transformer, the detection accuracy of the RGB-D salient object detection (SOD) model has …

RGB-D salient object detection task still encounters three challenges:(1) how to effectively
integrate superior information from different modalities,(2) how to effectively mine common …

Most existing tracking methods based on discriminative correlation filters (DCFs) update the
tracker every frame with a fixed learning rate. However, constantly adjusting the tracker can …

In this paper, a novel integrated long short‐term memory (LSTM) network and dynamic
update model are proposed for long‐term object tracking in video images. The LSTM …

Traditional tracking algorithms based on correlation filtering usually use a filter based on
Gaussian-like distribution to highlight the information of the target and weaken the …

The discriminative correlation filter (DCF)-based tracking method has shown good accuracy
and efficiency in visual tracking. However, the periodic assumption of sample space causes …