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）
Diffusion models have achieved remarkable success in image generation, yet their training is predominantly driven by full-reference objectives that enforce pixel-wise similarity to ground-truth images.Such supervision, while effective for fidelity, may insufficient in terms of subjective visual perception quality and text-image semantic consistency. In this work, we investigate the problem of incorporating no-reference perceptual quality into diffusion training. A key challenge is that directly optimizing perceptual signals, such as those provided by no-reference image quality assessment (NR-IQA) models, introduces a mismatch with the original diffusion objective, leading to training instability and distributional drift during fine-tuning. To address this issue, we propose an anchor-constrained optimization framework that enables stable perceptual adaptation. Specifically, we leverage a learned NR-IQA model as a perceptual guidance signal, while introducing an anchor-based regularization that enforces consistency with the base diffusion model in terms of noise prediction. This design effectively balances perceptual quality improvement and generative fidelity, allowing controlled adaptation toward perceptually favorable outputs without compromising the original generative behavior. Extensive experiments demonstrate that our method consistently enhances perceptual quality while preserving generation diversity and training stability, highlighting the effectiveness of anchor-constrained perceptual optimization for diffusion models.
Reliable perception is essential for robots that interact with the world. But sensors alone are often insufficient to provide this capability, and they are prone to errors due to various conditions in the environment. Furthermore, there is a need for robots to maintain a model of its surroundings even when objects go out of view and are no longer visible. This requires anchoring perceptual information onto symbols that represent the objects in the environment. In this paper, we present a model for action-aware perceptual anchoring that enables robots to track objects in a persistent manner. Ou…
Deep learning models are found to be vulnerable to adversarial examples, as wrong predictions can be caused by small perturbation in input for deep learning models. Most of the existing works of adversarial image generation try to achieve attacks for most models, while few of them make efforts on guaranteeing the perceptual quality of the adversarial examples. High quality adversarial examples matter for many applications, especially for the privacy preserving. In this work, we develop a framework based on the Minimum Noticeable Difference (MND) concept to generate adversarial privacy preservi…
OSOR: One-Step Diffusion Inpainting for Effect-Aware Object Removal
Shanghai Jiao Tong University（上海交通大学）；Tsinghua University（清华大学）；Xidian University（西安电子科技大学）；Tongji University（同济大学）；University of Electronic Science and Technology of China（电子科技大学）；Transsion（传音控股）
SkinGenBench: Generative Model and Preprocessing Effects for Synthetic Dermoscopic Augmentation in Melanoma Diagnosis
Alliance School of Advanced Computing, Alliance University, India（印度联盟大学高级计算学院）；Department of Computer Science and Communication, Østfold University College, 1783 Halden, Norway（挪威东福尔大学学院计算机科学与传播系）；Department of Dentistry and Oral Health, Aarhus University, 8000 Aarhus, Denmark（丹麦奥胡斯大学牙科与口腔健康系）
BeautyGRPO: Aesthetic Alignment for Face Retouching via Dynamic Path Guidance and Fine-Grained Preference Modeling
School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University, China（中山大学计算机科学与技术学院，深圳校区）；vivo BlueImage Lab, vivo Mobile Communication Co., Ltd., China（vivo蓝影实验室，vivo移动通信有限公司）
Semantic-Guided Cross-Sensor Super Resolution of Remote Sensing Images: A Gated Dual Conditioning Flow Matching Model
School of Computing and Augmented Intelligence（计算与增强智能学院）；School of Geographical Sciences and Urban Planning（地理科学与城市规划学院）；Arizona State University（亚利桑那州立大学）
Semantic-Structural Alignment for Generative Pictorial Charts
Visual Computing Research Center (VCC), College of Computer Science and Software Engineering (CSSE) Shenzhen University China（视觉计算研究中心（VCC）、计算机科学与软件工程学院（CSSE）深圳大学中国）
Group-of-Latents: Perceptual Video Compression at Extreme Bitrates via Masked Latent Generative Modeling
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University, Beijing, China（多媒体信息处理国家重点实验室，计算机科学学院，北京大学，北京，中国）；State Key Discipline Laboratory of Wide Band-Gap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an, China（宽带隙半导体技术重点学科实验室，微电子学院，西安电子科技大学，西安，中国）
RS-HyRe-R1: A Hybrid Reward Mechanism to Overcome Perceptual Inertia for Remote Sensing Images Understanding
School of Mechanical and Electrical Engineering, Central South University（中南大学机械与电气工程学院）；School of Geosciences and Info-Physics, Central South University（中南大学地球科学与信息物理学院）
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；East China Normal University（华东师范大学）
Pursuing realistic results according to human visual perception is the central concern in the image transformation tasks. Perceptual learning approaches like perceptual loss are empirically powerful for such tasks but they usually rely on the pre-trained classification network to provide features, which are not necessarily optimal in terms of visual perception of image transformation. In this paper, we argue that, among the features representation from the pre-trained classification network, only limited dimensions are related to human visual perception, while others are irrelevant, although b…
Dual-Latent Collaborative Decoding for Fidelity-Perception Balanced Image Compression
School of Information and Communication Engineering and the State Key Laboratory of Media Convergence and Communication, Communication University of China（信息与通信工程学院和媒体融合与通信国家重点实验室，中国通信大学）；School of Information Science and Electronic Engineering, Shanghai Jiao Tong University（信息科学与电子工程学院，上海交通大学）；Department of Computer Science, City University of Hong Kong（计算机科学系，香港城市大学）；State Key Laboratory of Multimedia information Processing, School of Computer Science, Peking University（多媒体信息处理国家重点实验室，计算机科学学院，北京大学）
Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare
City University of Hong Kong（香港城市大学）；Shanghai Jiao Tong University（上海交通大学）；Jiangxi University of Finance and Economics（江西财经大学）；Nanyang Technological University（南洋理工大学）
