With the rapid development of generative technologies, AI-Generated Images (AIGIs) have been widely applied in various aspects of daily life. However, due to the immaturity of the technology, the quality of the generated images varies, so it is important to develop quality assessment techniques for the generated images. Although some models have been proposed to assess the quality of generated images, they are inadequate when faced with the ever-increasing and diverse categories of generated images. Consequently, the development of more advanced and effective models for evaluating the quality of generated images is urgently needed. Recent research has explored the significant potential of the visual language model CLIP in image quality assessment, finding that it performs well in evaluating the quality of natural images. However, its application to generated images has not been thoroughly investigated. In this paper, we build on this idea and further explore the potential of CLIP in evaluating the quality of generated images. We design CLIP-AGIQA, a CLIP-based regression model for quality assessment of generated images, leveraging rich visual and textual knowledge encapsulated in CLIP. Particularly, we implement multi-category learnable prompts to fully utilize the textual knowledge in CLIP for quality assessment. Extensive experiments on several generated image quality assessment benchmarks, including AGIQA-3K and AIGCIQA2023, demonstrate that CLIP-AGIQA outperforms existing IQA models, achieving excellent results in evaluating the quality of generated images.
Boosting Image Restoration via Priors from Pre-trained Models
Zhejiang Lab（之江实验室）；CUHK（香港中文大学）；RealityEdge（睿迪智芯）；University of Macau（澳门大学）；Institute of Collaborative Innovation（协同创新研究院）；Texas A&M University（德克萨斯农工大学）；National University of Singapore（新加坡国立大学）；Zhejiang University（浙江大学）
Language Prompt vs. Image Enhancement: Boosting Object Detection With CLIP in Hazy Environments
College of Control Science and Engineering, China University of Petroleum (East China)（中国石油大学（华东）控制科学与工程学院）；School of Information Science and Engineering, Yunnan University（云南大学信息科学与工程学院）；Yunnan United Vision Technology Co., Ltd.（云南联合视觉科技有限公司）
Boosting Open-Vocabulary Object Detection by Handling Background Samples
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Nanjing Artificial Intelligence Research of IA（中科院南京人工智能研究院）
CLIP-PING: Boosting Lightweight Vision-Language Models with Proximus Intrinsic Neighbors Guidance
Kyung Hee University（庆熙大学）；The University of Danang—Vietnam-Korea University of Information and Communication Technology（岘港大学—越韩信息与通信技术大学）；Digital Science and Technology Institute（数字科学技术研究院）
Boosting Human-Object Interaction Detection with Text-to-Image Diffusion Model
Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）深圳大数据研究院）；International Digital Economy Academy (IDEA)（国际数字经济研究院（IDEA））
A Hybrid Defense Strategy for Boosting Adversarial Robustness in Vision-Language Models
Harbin Institute of Technology（哈尔滨工业大学）；University of Bristol（布里斯托大学）；Beijing University of Civil Engineering and Architecture（北京建筑大学）；University of Toronto Mississauga（多伦多大学密西沙加分校）；Xi’an Tieyi ICC（西安铁一中国际合作课程中心）
ProtoCLIP: Prototypical Contrastive Language Image Pretraining
College of Computer and Information, Hohai University（河海大学计算机与信息学院）；Science and Technology on Underwater Vehicle Technology Laboratory（水下航行器技术科技重点实验室）；MEGVII Research（旷视科技研究院）；Nanjing Research Institute of Electronic Engineering（南京电子工程研究所）；School of EECS, Peking University（北京大学电子工程与计算机科学学院）
Bridging Vision Foundation Model Priors with CLIP for Spatial-aware Few-shot Anomaly Detection in Medical Images
The Chinese University of Hong Kong（香港中文大学）；The University of Hong Kong（香港大学）；Institute of Medical Intelligence and XR, The Chinese University of Hong Kong（香港中文大学医学智能与扩展现实研究所）
Brought a Gun to a Knife Fight: Modern VFM Baselines Outgun Specialized Detectors on In-the-Wild AI Image Detection
Guangdong Provincial Key Laboratory of Intelligent Information Processing（广东省智能信息处理重点实验室）；Shenzhen Key Laboratory of Media Security（深圳媒体安全重点实验室）；SZU AFS Joint Innovation Center for AI Technology, Shenzhen University（深圳大学AFS人工智能技术联合创新中心）；University of North Texas（北卡罗来纳州立大学）；Academy of Forensic Science（法医科学研究院）；Nanchang University（南昌大学）
