Video saliency detection (VSD) aims at fast locating the most attractive
objects/things/patterns in a given video clip. Existing VSD-related works have mainly relied …

This article discusses the limitations of single-and two-modal salient object detection (SOD)
methods and the emergence of multi-modal SOD techniques that integrate Visible, Depth, or …

Convolutional neural network (CNN)-based salient object detection (SOD) models have
achieved promising performance in optical remote sensing images (ORSIs) in recent years …

One-stage object detection is a technique that uses a single deep neural network to detect
objects in an image or video. This method trains the network from start to end to recognize …

RGB-T salient object detection (SOD) has been developed rapidly and achieved excellent
results in recent years. However, some problems have not yet been solved. The current RGB …

Video salient object detection targets at extracting the most conspicuous objects in a video
sequence, which facilitate various video processing tasks, eg, video compression, video …

Visual transformer-based salient object detection (SOD) models have attracted increasing
research attention. However, the existing transformer-based RGB-D SOD models usually …

Object detection is one of the essential tasks in a variety of real-world applications such as
autonomous driving and robotics. In a real-world scenario, unfortunately, there are …

Salient object detection in optical remote sensing images presents great challenges due to
the characteristics of remote sensing images such as cluttered background, varying object …

Modern saliency detection models are based on the encoder-decoder framework and they
use different strategies to fuse the multi-level features between the encoder and decoder to …