Humans tend to mine objects by learning from a group of images or several frames of video
since we live in a dynamic world. In the computer vision area, many researchers focus on co …

Multivariate time series (MTS) forecasting plays an important role in the automation and
optimization of intelligent applications. It is a challenging task, as we need to consider both …

Infrared and visible image fusion has gradually proved to be a vital fork in the field of multi-
modality imaging technologies. In recent developments, researchers not only focus on the …

Recent years have witnessed a growing interest in co-object segmentation and multi-modal
salient object detection. Many efforts are devoted to segmenting co-existed objects among a …

In this paper, we present a weakly-supervised RGB-D salient object detection model via
scribble supervision. Specifically, as a multimodal learning task, we focus on effective …

Mirror segmentation, an emerging discipline in the field of computer vision, involves the
identification and marking of mirrors in an image. Current mirror segmentation methods rely …

Instance segmentation in biological images is an important task in the field of biological
images and biomedical analysis. Different from the instance segmentation of natural image …

Semi-supervised node classification with Graph Convolutional Network (GCN) is an
attractive topic in social media analysis and applications. Recent studies show that GCN …

Image co-segmentation and co-localization exploit inter-image information to identify and
extract foreground objects with a batch mode. However, they remain challenging when …

Existing methods in planar region segmentation suffer the problems of vague boundaries
and failure to detect small-sized regions. To address these, this study presents an end-to …