Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Ke, Zhanghan"'
Diffusion models trained on large-scale datasets have achieved remarkable progress in image synthesis. However, due to the randomness in the diffusion process, they often struggle with handling diverse low-level tasks that require details preservatio
Externí odkaz:
http://arxiv.org/abs/2403.00644
Removing shadows requires an understanding of both lighting conditions and object textures in a scene. Existing methods typically learn pixel-level color mappings between shadow and non-shadow images, in which the joint modeling of lighting and objec
Externí odkaz:
http://arxiv.org/abs/2402.00341
Recent semi-supervised learning (SSL) methods typically include a filtering strategy to improve the quality of pseudo labels. However, these filtering strategies are usually hand-crafted and do not change as the model is updated, resulting in a lot o
Externí odkaz:
http://arxiv.org/abs/2309.09774
In this paper, we present a Neural Preset technique to address the limitations of existing color style transfer methods, including visual artifacts, vast memory requirement, and slow style switching speed. Our method is based on two core designs. Fir
Externí odkaz:
http://arxiv.org/abs/2303.13511
As the core building block of vision transformers, attention is a powerful tool to capture long-range dependency. However, such power comes at a cost: it incurs a huge computation burden and heavy memory footprint as pairwise token interaction across
Externí odkaz:
http://arxiv.org/abs/2303.08810
Autor:
Liu, Yuhao, Guo, Qing, Fu, Lan, Ke, Zhanghan, Xu, Ke, Feng, Wei, Tsang, Ivor W., Lau, Rynson W. H.
Existing deep learning-based shadow removal methods still produce images with shadow remnants. These shadow remnants typically exist in homogeneous regions with low-intensity values, making them untraceable in the existing image-to-image mapping para
Externí odkaz:
http://arxiv.org/abs/2301.03182
Recent works on image harmonization solve the problem as a pixel-wise image translation task via large autoencoders. They have unsatisfactory performances and slow inference speeds when dealing with high-resolution images. In this work, we observe th
Externí odkaz:
http://arxiv.org/abs/2207.01322
To address the challenging portrait video matting problem more precisely, existing works typically apply some matting priors that require additional user efforts to obtain, such as annotated trimaps or background images. In this work, we observe that
Externí odkaz:
http://arxiv.org/abs/2109.11818
Existing portrait matting methods either require auxiliary inputs that are costly to obtain or involve multiple stages that are computationally expensive, making them less suitable for real-time applications. In this work, we present a light-weight m
Externí odkaz:
http://arxiv.org/abs/2011.11961
We investigate the generalization of semi-supervised learning (SSL) to diverse pixel-wise tasks. Although SSL methods have achieved impressive results in image classification, the performances of applying them to pixel-wise tasks are unsatisfactory d
Externí odkaz:
http://arxiv.org/abs/2008.05258