Generating an image from a provided descriptive text is quite a challenging task because of the difficulty in incorporating perceptual information (object shapes, colors, and their interactions) along with providing high relevancy related to the provided text. Current methods first generate an initial low-resolution image, which typically has irregular object shapes, colors, and interaction between objects. This initial image is then improved by conditioning on the text. However, these methods mainly address the problem of using text representation efficiently in the refinement of the initially generated image, while the success of this refinement process depends heavily on the quality of the initially generated image, as pointed out in the DM-GAN paper. Hence, we propose a method to provide good initialized images by incorporating perceptual understanding in the discriminator module. We improve the perceptual information at the first stage itself, which results in significant improvement in the final generated image. In this paper, we have applied our approach to the novel StackGAN architecture. We then show that the perceptual information included in the initial image is improved while modeling image distribution at multiple stages. Finally, we generated realistic multi-colored images conditioned by text. These images have good quality along with containing improved basic perceptual information. More importantly, the proposed method can be integrated into the pipeline of other state-of-the-art text-based-image-generation models to generate initial low-resolution images. We also worked on improving the refinement process in StackGAN by augmenting the third stage of the generator-discriminator pair in the StackGAN architecture. Our experimental analysis and comparison with the state-of-the-art on a large but sparse dataset MS COCO further validate the usefulness of our proposed approach.
TIQA: Human-Aligned Perceptual Text Quality Assessment in Generated Images
Lomonosov Moscow State University（洛蒙诺索夫莫斯科国立大学）；ISP RAS Research Center for Trusted Artificial Intelligence（俄罗斯科学院信息与系统研究所在可信人工智能领域研究中心）；MSU Institute for Artificial Intelligence（莫斯科大学人工智能研究所）
Shanghai Jiaotong University（上海交通大学）；Media Experience and Evaluation Lab（媒体体验与评估实验室）；Nanyang Technological University（南洋理工大学）；East China Normal University（华东师范大学）
ACPO: Anchor-Constrained Perceptual Optimization for Diffusion Models with No-Reference Quality Guidance
School of Electronic and Information Engineering, Anhui University（安徽大学电子与信息工程学院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥国家综合科学中心人工智能研究院）；School of Cyber Science and Technology, University of Science and Technology of China（中国科学技术大学网络科学与技术学院）
UniPercept: Towards Unified Perceptual-Level Image Understanding across Aesthetics, Quality, Structure, and Texture
University of Science and Technology of China（中国科学技术大学）；Shanghai AI Laboratory（上海人工智能实验室）；Peking University（北京大学）；Shanghai Jiao Tong University（上海交通大学）；Tsinghua University（清华大学）；Nanjing University（南京大学）；Sun Yat-sen University（中山大学）；Tele-AI Website（Tele-AI网站）
Textural-Perceptual Joint Learning for No-Reference Super-Resolution Image Quality Assessment
School of Software, Dalian University of Technology（大连理工大学软件学院）；International School of Information Science and Engineering, Dalian University of Technology（大连理工大学国际信息与工程学院）
PhishSnap: Image-Based Phishing Detection Using Perceptual Hashing
Dept. of CSE United International University Dhaka, Bangladesh（计算机科学与工程系，国际联合大学，达卡，孟加拉国）；Professor, Dept. of CSE United International University Dhaka, Bangladesh（计算机科学与工程系，国际联合大学，达卡，孟加拉国）
Perceptual Image Compression with Cooperative Cross-Modal Side Information
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Peng Cheng Laboratory（鹏城实验室）；Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies（广东省新型安全智能重点实验室）；Huawei Technologies Company Ltd（华为技术有限公司）；Shenzhen University（深圳大学）
A new Image Similarity Metric for a Perceptual and Transparent Geometric and Chromatic Assessment
Institute for High-Performance Computing and Networking (ICAR) - National Research Council (CNR)（高性能计算与网络研究所（ICAR）- 国家研究理事会（CNR））；University of Naples Federico II（那不勒斯费德里科二世大学）；University of Naples Parthenope（那不勒斯帕尔泰诺大学）
Perceptual Robust Hashing for Color Images with Canonical Correlation Analysis
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology（上海理工大学光电信息与计算机工程学院）；School of Computer Science, Fudan University（复旦大学计算机科学技术学院）
SVGEval: A Vision-Grounded Framework for Perceptual-Quality Benchmarking and Evaluation in Text-to-SVG Generation
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））；School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University（上海交通大学集成电路学院（信息科学与工程学院））；Zhiyuan College, Shanghai Jiao Tong University（上海交通大学致远学院）
