相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。
Article
Computer Science, Software Engineering
Shu-Yu Chen et al.
Summary: This research presents a novel semiautomatic image colorization method that achieves coherent colorization of a set of images using a single colorized reference image. By utilizing an active-learning framework to match local regions and mixed-integer quadratic programming to consider spatial contexts, the method effectively refines the matching results and achieves high accuracy in the final colorized images. Additionally, experiments show that this method outperforms the current state-of-the-art deep learning based colorization method in terms of color coherency with the reference image.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2022)
Article
Computer Science, Artificial Intelligence
Yu Zhang et al.
Summary: This paper provides a survey of Multi-Task Learning (MTL) from the perspective of algorithmic modeling, applications, and theoretical analyses. It discusses different MTL algorithms and their characteristics, as well as the combination of MTL with other learning paradigms. The paper also reviews MTL models for large-scale tasks or high-dimensional data, as well as dimensionality reduction and feature hashing. Real-world applications of MTL are examined, and theoretical analyses and future directions are discussed.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Liangyu Chen et al.
Summary: This paper proposes a simple and computationally efficient baseline method that outperforms state-of-the-art methods in image restoration. By eliminating the need for nonlinear activation functions, the proposed method achieves better results with lower computational costs. The method achieves state-of-the-art results on challenging benchmarks.
COMPUTER VISION, ECCV 2022, PT VII
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Ziyu Wan et al.
Summary: We propose a learning-based framework called recurrent transformer network (RTN) for restoring heavily degraded old films. Our method utilizes the hidden knowledge learned from adjacent frames to ensure temporal coherency and effectively restore challenging artifacts. The framework also allows for unsupervised scratch position inference, which generalizes well to real-world degradations. Experimental results demonstrate the significant superiority of RTN over existing solutions on both synthetic and real-world old films.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Long Ma et al.
Summary: This paper proposes a new Self-Calibrated Illumination (SCI) learning framework for enhancing image brightness in real-world low-light scenarios. The framework achieves a balance between computational efficiency and visual quality through cascaded illumination learning, weight sharing, and a self-calibrated module.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Yuda Song et al.
Summary: In this paper, a deep learning-based image enhancement method called StarEnhancer is proposed to cover multiple tonal styles using a single model. Users can customize the model with a one-time setting to make the enhanced images more in line with their aesthetics. The method outperforms contemporary single-style image enhancement methods in terms of processing speed and quality metrics.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Kibeom Hong et al.
Summary: The study introduces a Domain-aware Style Transfer Networks (DSTN) that not only transfers style from a given reference image but also transfers the domain property. By designing a novel domainness indicator and introducing domain-aware skip connection, the model achieves better qualitative results and outperforms previous methods.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Mahmoud Afifi et al.
Summary: This paper introduces HistoGAN, a color histogram-based method for controlling the colors of GAN-generated images. By modifying the StyleGAN architecture effectively, the authors propose a way to control the colors of GAN-generated images based on target color histogram features. In addition, they demonstrate how HistoGAN can be expanded for recoloring real images with an unsupervised approach called ReHistoGAN.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Man M. Ho et al.
Summary: This work focuses on learning low-level image transformation, especially color-shifting methods, and introduces a novel supervised approach for color style transfer. Experimental results demonstrate that Deep Preset outperforms previous works in color style transfer both quantitatively and qualitatively.
2021 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION WACV 2021
(2021)
Article
Computer Science, Hardware & Architecture
Ian Goodfellow et al.
COMMUNICATIONS OF THE ACM
(2020)
Article
Computer Science, Software Engineering
Junyong Lee et al.
Article
Computer Science, Software Engineering
Mingming He et al.
ACM TRANSACTIONS ON GRAPHICS
(2019)
Article
Computer Science, Software Engineering
Satoshi Iizuka et al.
ACM TRANSACTIONS ON GRAPHICS
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Bo Zhang et al.
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019)
(2019)
Article
Computer Science, Software Engineering
Michael Gharbi et al.
ACM TRANSACTIONS ON GRAPHICS
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Christian Ledig et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)
(2017)
Article
Computer Science, Artificial Intelligence
Lin Zhang et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2011)
Article
Computer Science, Software Engineering
E Reinhard et al.
IEEE COMPUTER GRAPHICS AND APPLICATIONS
(2001)
