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VALSE 论文速览 第102期:基于对偶对抗自适应的跨设备真实图像超分辨率 ...

2022-11-18 17:18| 发布者: 程一-计算所| 查看: 87| 评论: 0

摘要: 为了使得视觉与学习领域相关从业者快速及时地了解领域的最新发展动态和前沿技术进展,VALSE最新推出了《论文速览》栏目,将在每周发布一至两篇顶会顶刊论文的录制视频,对单个前沿工作进行细致讲解。本期VALSE论文速 ...

为了使得视觉与学习领域相关从业者快速及时地了解领域的最新发展动态和前沿技术进展,VALSE最新推出了《论文速览》栏目,将在每周发布一至两篇顶会顶刊论文的录制视频,对单个前沿工作进行细致讲解。本期VALSE论文速览选取了来自中山大学的真实图像超分辨率方面的工作。该工作由魏朋旭副研究员和李冠彬副教授共同指导许晓倩硕士完成,由论文通讯作者魏朋旭副研究员录制。


论文题目:Dual Adversarial Adaptation for Cross-Device Real-World Image Super-Resolution

作者列表:许晓倩†(中山大学)、魏朋旭*(中山大学)、陈伟凯(腾讯美国)、刘阳(中山大学)、毛明志(中山大学)、林倞(中山大学)、李冠彬(中山大学)

B站观看网址:

https://www.bilibili.com/video/BV1584y1v7T8/



论文摘要:

Due to the sophisticated imaging process, an identical scene captured by different cameras could exhibit distinct imaging patterns, introducing distinct proficiency among the super-resolution (SR)models trained on images from different devices. In this paper, we investigate a novel and practical task coded cross-device SR, which strives to adapt a real-world SR model trained on the paired images captured by one camera to low-resolution (LR)images captured by arbitrary target devices. The proposed task is highly challenging due to the absence of paired data from various imaging devices. To address this issue, we propose an unsupervised domain adaptation mechanism for real-world SR, named Dual ADversarial Adaptation (DADA), which only requires LR images in the target domain with available real paired data from a source camera. DADA employs the Domain-Invariant Attention (DIA)module to establish the basis of target model training even without HR supervision. Furthermore, the dual framework of DADA facilitates an Inter-domain Adversarial Adaptation (InterAA)in one branch for two LR input images from two domains, and an Intra-domain Adversarial Adaptation (IntraAA)in two branches for an LR input image. InterAA and IntraAA together improve the model transferability from the source domain to the target. We empirically conduct experiments under six Real to Real adaptation settings among three different cameras, and achieve superior performance compared with existing state-of-the-art approaches. We also evaluate the proposed DADA to address the adaptation to the video camera, which presents a promising research topic to promote the wide applications of real-world super-resolution.


论文信息:

[1] X. Xu, P. Wei, W. Chen, Y. Liu, M. Mao, L. Lin, and G. Li. Dual Adversarial Adaptation for Cross-Device Real-World Image Super-Resolution. In CVPR, 2022.


论文链接:

[https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Dual_Adversarial_Adaptation_for_Cross-Device_Real-World_Image_Super-Resolution_CVPR_2022_paper.pdf]


代码链接:

[https://github.com/lonelyhope/DADA]


视频讲者简介:

魏朋旭,中山大学计算机学院副研究员。主要研究方向为计算机视觉,尤其是真实图像超分辨率数据基准及方法的研究,以及视觉模型的鲁棒性和可迁移。在TPAMI、CVPR等顶级期刊和会议发表多篇论文。



特别鸣谢本次论文速览主要组织者:

月度轮值AC:李冠彬 (中山大学)、李镇 (香港中文大学 (深圳))

季度责任AC:张姗姗 (南京理工大学)


活动参与方式

1、VALSE每周举行的Webinar活动依托B站直播平台进行,欢迎在B站搜索VALSE_Webinar关注我们!

直播地址:

https://live.bilibili.com/22300737;

历史视频观看地址:

https://space.bilibili.com/562085182/ 


2、VALSE Webinar活动通常每周三晚上20:00进行,但偶尔会因为讲者时区问题略有调整,为方便您参加活动,请关注VALSE微信公众号:valse_wechat 或加入VALSE QQ R群,群号:137634472);


*注:申请加入VALSE QQ群时需验证姓名、单位和身份缺一不可。入群后,请实名,姓名身份单位。身份:学校及科研单位人员T;企业研发I;博士D;硕士M。


3、VALSE微信公众号一般会在每周四发布下一周Webinar报告的通知。


4您也可以通过访问VALSE主页:http://valser.org/ 直接查看Webinar活动信息。Webinar报告的PPT(经讲者允许后),会在VALSE官网每期报告通知的最下方更新。

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