Abstract Deep Learning-based Unsupervised Salient Object Detection (USOD) mainly relies
on the noisy saliency pseudo labels that have been generated from traditional handcraft …

For the video salient object detection (VSOD) task, how to excavate the information from the
appearance modality and the motion modality has always been a topic of great concern. The …

The existing state-of-the-art (SOTA) video salient object detection (VSOD) models have
widely followed short-term methodology, which dynamically determines the balance …

Image saliency detection, to which much effort has been devoted in recent years, has
advanced significantly. In contrast, the community has paid little attention to video saliency …

Scribble annotations have recently become popular in video salient object detection.
Previous methods only focus on utilizing shallow feature consistency for more integral …

Video salient object detection (VSOD) aims at locating the most attractive objects presented
in video sequences by exploiting spatial and temporal cues. Previous methods mainly utilize …

As moving objects always draw more attention of human eyes, the temporal motion
information is always exploited complementarily with spatial information to detect salient …

In dynamic environments, several simultaneous localization and mapping (SLAM) systems
effectively utilize optical flow fields to distinguish dynamic from static feature points …

Most of the cutting-edge 3D convolutional-based video saliency prediction models adopt a
fully convolutional encoder-decoder architecture, which provides a good spatiotemporal …

This paper focuses on the Unsupervised Salient Object Detection (USOD) issue. We come
up with a two-stage Activation-to-Saliency (A2S) framework that effectively excavates …