Image segmentation refers to the process to divide an image into meaningful non-
overlapping regions according to human perception, which has become a classic topic since …

Co-saliency detection is a newly emerging and rapidly growing research area in the
computer vision community. As a novel branch of visual saliency, co-saliency detection …

Text-to-image diffusion models have made significant advances in generating and editing
high-quality images. As a result, numerous approaches have explored the ability of diffusion …

We study the use of deep features extracted from a pretrained Vision Transformer (ViT) as
dense visual descriptors. We observe and empirically demonstrate that such features, when …

The success of deep learning in vision can be attributed to:(a) models with high capacity;(b)
increased computational power; and (c) availability of large-scale labeled data. Since 2012 …

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 …

We propose an end-to-end-trainable attention module for convolutional neural network
(CNN) architectures built for image classification. The module takes as input the 2D feature …

We address the problem of determining correspondences between two images in
agreement with a geometric model such as an affine or thin-plate spline transformation, and …

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 …