Personalized Image Aesthetics Assessment

Follow this topic Ask about this topic

Momentum

0 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 10

All topics

Global Attention-Fused Image Cropping with Attention-Guided and Global-Aligned Crop Evaluator
Aug 5, 2026Haotian Yang, Zhile Yang, Kin-Man Lam +2Personalized Image Aesthetics Assessment Image Editing
Faculty of Data Science, City University of Macau, Macao, China · Shenzhen University of Advanced Technology, Shenzhen, China · Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China +2
DODA: A Database of Datasets for Aesthetics Research
Jul 30, 2026Lisa Koßmann, Ralf Bartho, Christoph Redies +1Personalized Image Aesthetics Assessment
Laboratory of Experimental Psychology, Department of Brain and Cognition, University of Leuven (KU Leuven), Leuven, Belgium · Leuven AI Institute, University of Leuven (KU Leuven), Leuven, Belgium · Experimental Aesthetics Group, Institute for Anatomy I, University Hospital Jena, Germany
Learning Color Grading, No Photo Sharing: Federated Aesthetic Preference Learning for Personalized Image Enhancement
Jul 30, 2026Chuanzhi Xu, Ziyuan Tao, Jean Julien KNell +5Personalized Image Aesthetics Assessment Image Enhancement
The University of Sydney · Macquarie University
Personalized Image Aesthetic Assessment via Preference-rich Sample Mining and Cohort Merging
Jul 17, 2026Zhichao Yang, Tianjiao Gu, Zhixianhe Zhang +3Personalized Image Aesthetics Assessment Image Quality Assessment
Xidian University Xi’an, China · Xidian University Xian, China
XPASS-Vis: A Dataset for Cross-Domain Personalized Image Aesthetic Assessment
Jun 14, 2026Takato Hayashi, Hiroaki Takahara, Candy Olivia Mawalim +4Personalized Image Aesthetics Assessment Cross-Domain Generalization
Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan · Communication Science Laboratories, NTT, Inc.,2026 Atsugi-shi 243-0198, Japan
Beyond Absolute Scores: Relative Edit-induced Difference for Generalizable Image Aesthetic Assessment
Jun 4, 2026Qifei Jia, Xintong Yao, Yasen Zhang +7Personalized Image Aesthetics Assessment Image Quality Assessment
Xiaomi Corporation, Beijing, China
ERNIE-Image Technical Report
May 25, 2026Jiaxiang Liu, Zhida Feng, Pengyu Zou +46Personalized Image Aesthetics Assessment Diffusion Model Distillation
Preferences Order, Ratings Anchor: From Fused Expert Aesthetic Ground Truth to Self-Distillation
May 19, 2026Yuanpei Zhao, Jie Lin, Chao Zhang +5Pairwise Preference Learning Personalized Image Aesthetics Assessment
1Sichuan University · 2NetEase Fuxi AI Lab · †Work done during an internship at NetEase Fuxi AI Lab. +1
AI Outperforms Humans in Personalized Image Aesthetics Assessment via LLM-Based Interviews and Semantic Feature Extraction
May 14, 2026Yoshia Abe, Tatsuya Daikoku, Yasuo KuniyoshiPersonalized Image Aesthetics Assessment Image Quality Assessment
Graduate School of Information Science and Technology The University of Tokyo Bunkyo-ku, Tokyo 113-8654, Japan
Enhancing Zero-shot Personalized Image Aesthetics Assessment with Profile-aware Multimodal LLM
Apr 19, 2026Chun Wang, Chenfeng Wei, Chenyang Liu +1Zero-Shot Learning Personalized Image Aesthetics Assessment
Mashang Consumer Finance Co., Ltd., Chongqing, China · Xi’an Jiaotong-Liverpool University, Suzhou, China · Beijing University of Posts and Telecommunications, Beijing, China

Aug 5, 2026, cs.CVJ/K move · Enter open · S save

Global Attention-Fused Image Cropping with Attention-Guided and Global-Aligned Crop Evaluator

Haotian Yang, Zhile Yang, Kin-Man Lam, Patrick Le Callet+1

Faculty of Data Science, City University of Macau, Macao, China · Shenzhen University of Advanced Technology, Shenzhen, China · Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China+2

Personalized Image Aesthetics Assessment Image Editing

Open paper AskPDF arXiv

Image cropping aims to improve image aesthetics by preserving important content within an appropriately composed region. However, most existing methods focus primarily on salient regions and therefore have limited sensitivity to the global relationships among the main image components. To address this limitation, we propose Global Attention-Fused Image Cropping (GAFIC), which consists of an Attention-Guided Feature Fusion (AGFF) and a Global-Aligned Crop Evaluator (GACE). AGFF aggregates the importance of local regions to construct a global representation that captures both image structure and local details. GACE aligns candidate crop features with this global representation, enabling crop evaluation to remain sensitive to boundary changes. We further combine three ranking losses across multiple scales to obtain accurate and stable crop scores. Extensive experiments on the GAIC and CPC datasets demonstrate that GAFIC outperforms existing image-cropping methods, particularly in terms of accuracy and stability. Unlike pixel-level retargeting methods such as seam carving, inpainting, and diffusion-based synthesis, GAFIC does not synthesize or modify the retained pixels; instead, it selects an aesthetically preferred crop from the source image, making it suitable for scenarios where pixel integrity and efficient batch processing are important. The source code is available at https://github.com/AIVRC/GAFIC.git.

Momentum

Related topics

Latest papers 10