Finding correspondences between images is a fundamental problem in computer vision. In
this paper, we show that correspondence emerges in image diffusion models without any …

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 …

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 …

Abstract Temporal Action Detection (TAD) aims to accurately capture each action interval in
an untrimmed video and to understand human actions. This paper comprehensively surveys …

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 …

RGB-D based salient object detection (SOD) methods leverage the depth map as a valuable
complementary information for better SOD performance. Previous methods mainly resort to …

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 increasing demand for virtual reality applications has highlighted the significance of
crafting immersive 3D assets. We present a text-to-3D 360∘ scene generation pipeline that …