Npix2Cpix: A GAN-Based Image-to-Image Translation Network With Retrieval- Classification Integration for Watermark Retrieval From Historical Document Images
BRAC University（BRAC大学）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）
Utsab Saha, Sawradip Saha, Shaikh Anowarul Fattah, Mohammad Saquib
The identification and restoration of ancient watermarks have long been a major topic in codicology and history. Classifying historical documents based on watermarks is challenging due to their diversity, noisy samples, multiple representation modes, and minor distinctions between classes and intra-class variations. This paper proposes a modified U-net-based conditional generative adversarial network (GAN) named Npix2Cpix to translate noisy raw historical watermarked images into clean, handwriting-free watermarked images by performing image translation from degraded (noisy) pixels to clean pixels. Using image-to-image translation and adversarial learning, the network creates clutter-free images for watermark restoration and categorization. The generator and discriminator of the proposed GAN are trained using two separate loss functions, each based on the distance between images, to learn the mapping from the input noisy image to the output clean image. After using the proposed GAN to pre-process noisy watermarked images, Siamese-based one-shot learning is employed for watermark classification. Experimental results on a large-scale historical watermark dataset demonstrate that cleaning the noisy watermarked images can help to achieve high one-shot classification accuracy. The qualitative and quantitative evaluation of the retrieved watermarked image highlights the effectiveness of the proposed approach.
DSE-GAN: Dynamic Semantic Evolution Generative Adversarial Network for Text-to-Image Generation
University of Science and Technology of China（中国科学技术大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
针对多阶段文生图中静态文本语义指导不足的问题，本文提出 DSE-GAN，通过 DSE 模块依据历史图文反馈动态重组语义，并结合 SAMA 架构提升生成质量。
Unified High-binding Watermark for Unconditional Image Generation Models
Beijing Institute of Technology（北京理工大学）；School of Cyberspace Science and Technology, Beijing Institute of Technology（北京理工大学网络空间科学与技术学院）；School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）
Aggregated Multi-GANs for Controlled 3D Human Motion Prediction
Zhejiang Gongshang University（浙江工商大学）；Jilin University（吉林大学）；Nanyang Technological University（南洋理工大学）；University of Science and Technology of China（中国科学技术大学）；Zhejiang University（浙江大学）
RNDiff: Rainfall nowcasting with Condition Diffusion Model
Faculty of Artificial Intelligence and Big Data（人工智能与大数据学院）；Chongqing University of Technology（重庆理工大学）；Yibin University（宜春大学）；Chengdu University of Information Technology（成都信息工程大学）
Dynamically Masked Discriminator for Generative Adversarial Networks
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；National University of Singapore（新加坡国立大学）；Tencent YouTu Lab（腾讯优图实验室）；Guangxi Medical University（广西医科大学）
Generative Adversarial Networks Bridging Art and Machine Intelligence
Imperial College London（帝国理工学院）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）；Indiana University（印第安纳大学）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；Georgia Institute of Technology（佐治亚理工学院）；Kyoto University（京都大学）；AppCubic；Rutgers University（罗格斯大学）；Purdue University（普渡大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；National Taiwan Normal University（台湾师范大学）；University of Hawaii（夏威夷大学）；Hong Kong University of Science and Technology（香港科技大学）；School of Visual Arts（视觉艺术学院）；Emory University（埃默里大学）；Aarhus University（奥胡斯大学）；Zhejiang University（浙江大学）；University of Edinburgh（爱丁堡大学）；National Tsing Hua University（国立清华大学）；University of Manchester（曼彻斯特大学）；Pingtan Research Institute of Xiamen University（厦门大学平潭研究院）；University of Liverpool（利物浦大学）；JTB Technology Corp.（JTB科技公司）
Georgia Institute of Technology（佐治亚理工学院）；H. Milton Stewart School of Industrial and Systems Engineering, Georgia Tech（佐治亚理工学院H.米尔顿·斯图尔特工业与系统工程学院）；School of Computational Science and Engineering, Georgia Tech（佐治亚理工学院计算科学与工程学院）；Department of Industrial Engineering and Engineering Management, National Tsing Hua University（国立清华大学工业工程与工程管理系）；Marcus Heart Valve Center, Piedmont Heart Institute（皮埃蒙特心脏研究所马库斯心脏瓣膜中心）；School of Materials Science and Engineering, Georgia Tech（佐治亚理工学院材料科学与工程学院）
Technical University of Munich（慕尼黑工业大学）；Stanford University（斯坦福大学）；Max-Planck-Institute for Informatics（马克斯·普朗克信息学研究所）；University of Erlangen-Nuremberg（埃尔朗根-纽伦堡大学）
AI-GenBench: A New Ongoing Benchmark for AI-Generated Image Detection
Dipartimento di Informatica - Scienza e Ingegneria (DISI) Università di Bologna（博洛尼亚大学信息学院-科学与工程系）；Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione (DIETI) Università degli Studi di Napoli Federico II（那不勒斯费德里科二世大学电子工程与信息科技系）；IdentifAI
