Image segmentation, which has become a research hotspot in the field of image processing
and computer vision, refers to the process of dividing an image into meaningful and non …

Detecting and segmenting salient objects from natural scenes, often referred to as salient
object detection, has attracted great interest in computer vision. While many models have …

Semantic segmentation has a broad range of applications, but its real-world impact has
been significantly limited by the prohibitive annotation costs necessary to enable …

In this article, we model a set of pixelwise object segmentation tasks—automatic video
segmentation (AVS), image co-segmentation (ICS) and few-shot semantic segmentation …

This work proposes a novel attentive graph neural network (AGNN) for zero-shot video
object segmentation (ZVOS). The suggested AGNN recasts this task as a process of iterative …

Low-shot learning methods for image classification support learning from sparse data. We
extend these techniques to support dense semantic image segmentation. Specifically, we …

Over the past few years, state-of-the-art image segmentation algorithms are based on deep
convolutional neural networks. To render a deep network with the ability to understand a …

Semi-supervised learning has a great potential in medical image segmentation tasks with a
few labeled data, but most of them only consider single-modal data. The excellent …

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 …

Learning how objects sound from video is challenging, since they often heavily overlap in a
single audio channel. Current methods for visually-guided audio source separation sidestep …