Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Van Gansbeke, Wouter"'
Generalist vision models aim for one and the same architecture for a variety of vision tasks. While such shared architecture may seem attractive, generalist models tend to be outperformed by their bespoken counterparts, especially in the case of pano
Externí odkaz:
http://arxiv.org/abs/2408.16504
With recent advances in image and video diffusion models for content creation, a plethora of techniques have been proposed for customizing their generated content. In particular, manipulating the cross-attention layers of Text-to-Image (T2I) diffusio
Externí odkaz:
http://arxiv.org/abs/2404.05519
Panoptic and instance segmentation networks are often trained with specialized object detection modules, complex loss functions, and ad-hoc post-processing steps to manage the permutation-invariance of the instance masks. This work builds upon Stable
Externí odkaz:
http://arxiv.org/abs/2401.10227
The task of unsupervised semantic segmentation aims to cluster pixels into semantically meaningful groups. Specifically, pixels assigned to the same cluster should share high-level semantic properties like their object or part category. This paper pr
Externí odkaz:
http://arxiv.org/abs/2206.06363
Contrastive self-supervised learning has outperformed supervised pretraining on many downstream tasks like segmentation and object detection. However, current methods are still primarily applied to curated datasets like ImageNet. In this paper, we fi
Externí odkaz:
http://arxiv.org/abs/2106.05967
Being able to learn dense semantic representations of images without supervision is an important problem in computer vision. However, despite its significance, this problem remains rather unexplored, with a few exceptions that considered unsupervised
Externí odkaz:
http://arxiv.org/abs/2102.06191
Autor:
Van Gansbeke, Wouter, Vandenhende, Simon, Georgoulis, Stamatios, Proesmans, Marc, Van Gool, Luc
Can we automatically group images into semantically meaningful clusters when ground-truth annotations are absent? The task of unsupervised image classification remains an important, and open challenge in computer vision. Several recent approaches hav
Externí odkaz:
http://arxiv.org/abs/2005.12320
Autor:
Vandenhende, Simon, Georgoulis, Stamatios, Van Gansbeke, Wouter, Proesmans, Marc, Dai, Dengxin, Van Gool, Luc
With the advent of deep learning, many dense prediction tasks, i.e. tasks that produce pixel-level predictions, have seen significant performance improvements. The typical approach is to learn these tasks in isolation, that is, a separate neural netw
Externí odkaz:
http://arxiv.org/abs/2004.13379
Autonomous cars need continuously updated depth information. Thus far, depth is mostly estimated independently for a single frame at a time, even if the method starts from video input. Our method produces a time series of depth maps, which makes it a
Externí odkaz:
http://arxiv.org/abs/2001.02613
This work proposes a new method to accurately complete sparse LiDAR maps guided by RGB images. For autonomous vehicles and robotics the use of LiDAR is indispensable in order to achieve precise depth predictions. A multitude of applications depend on
Externí odkaz:
http://arxiv.org/abs/1902.05356