Salient object detection models mimic the behavior of human beings and capture the most
salient region/object from the images or scenes, this field contains many important …

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 …

The goal of co-salient object detection (CoSOD) is to discover salient objects that commonly
appear in a query group containing two or more relevant images. Therefore, how to …

In this article, we conduct a comprehensive study on the co-salient object detection (CoSOD)
problem for images. CoSOD is an emerging and rapidly growing extension of salient object …

The detection results of many existing co-saliency detection methods are easily interfered by
the unrelated salient objects, which have similar appearance characteristics to co-salient …

We present a novel group collaborative learning framework (GCNet) capable of detecting co-
salient objects in real time (16ms), by simultaneously mining consensus representations at …

Co-salient object detection, with the target of detecting co-existed salient objects among a
group of images, is gaining popularity. Recent works use the attention mechanism or extra …

Abstract Referring Expression Segmentation (RES) is a widely explored multi-modal task,
which endeavors to segment the pre-existing object within a single image with a given …

Co-saliency detection aims to discover the common and salient foregrounds from a group of
relevant images. For this task, we present a novel adaptive graph convolutional network with …

Abstract Generalized Few-shot Semantic Segmentation (GFSS) extends Few-shot Semantic
Segmentation (FSS) to simultaneously segment unseen classes and seen classes during …