arXiv AI
Jul 10
Leveraging Color Naming for Image Enhancement
arXiv:2607. 08185v1 Announce Type: cross Abstract: Enhancing images to make them visually appealing is a persistent challenge in computer vision.
Learning Color Grading, No Photo Sharing: Federated Aesthetic Preference Learning for Personalized Image Enhancement
Personalized image enhancement should reflect individual aesthetic taste, yet learning such preferences commonly depends on private photos and ratings that are unsuitable for centralized collection. The task must infer preference from sparse, heterogeneous feedback and translate it into natural-looking color transformations on resource-constrained user devices.
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arXiv AI
Jun 9
ZIPP:Zero-shot Image Personalization from Personas
arXiv:2606. 08841v1 Announce Type: new Abstract: Text-to-image diffusion models are increasingly deployed in open-ended creative contexts, yet their outputs remain impersonal, optimized for aggregate aesthetics rather than individual taste.
Hugging Face Trending Papers
Jun 4
Beyond Absolute Scores: Relative Edit-induced Difference for Generalizable Image Aesthetic Assessment
Traditional Image Aesthetic Assessment (IAA) methods mainly rely on regressing absolute Mean Opinion Scores (MOS). However, such a paradigm overlooks the inherently dynamic nature of human aesthetic perception, which relies on subconscious comparison against implicit visual references.
arXiv AI
2d ago
Personalized Image Generation with Reasoning and Reflection
arXiv:2610.00737v1 Announce Type: cross Abstract: Personalized image generation has remained narrowly focused on conditional synthesis from curated visual exemplars, rather than capturing who a user...
arXiv Computer Vision
Sep 21
Adaptive Color Grading
arXiv:2609.21169v1 Announce Type: cross Abstract: Independent control of tonescale regions (e.g., shadows, highlights) is essential for painters, photographers and cinematographers to bring 2D images...
arXiv Computer Vision
Sep 15
MCIQA-2K: A Multi-Dimensional Dataset and No-Reference Quality Assessment Benchmark for Colorized Images
arXiv:2609.14495v1 Announce Type: new Abstract: Image colorization is an inherently ill-posed task, since a single grayscale image may correspond to multiple plausible colorized results. Consequently...
arXiv AI
3d ago
ReGain: Restoring Subject Fidelity in Personalization on Synthetic Images
ReGain is a training‑free correction that improves subject fidelity in text‑to‑image diffusion models personalized with synthetic images. The authors show that fine‑tuning on synthetic images degrades fidelity due to inflated classifier‑free guidance, especially at high frequencies. ReGain measures this inflation per frequency band and scales it down during sampling, closing 51‑64% of the fidelity gap on Stable Diffusion v1.5 and improving performance on SDXL and SD 3.5 while preserving text alignment.
arXiv AI
Sep 15
Efficient Personalization of Generative User Interfaces
The paper "Efficient Personalization of Generative User Interfaces" addresses the challenge of tailoring generative user interfaces (GenUIs) to individual users when interface screens are not pre‑defined. By collecting judgments from 20 participants on 600 GenUI pairs, the authors show low agreement (Krippendorff's alpha = 0.25) and diverse rationales for UI preferences. They propose a sample‑efficient personalization method that leverages a few pairwise judgments to weight prior users’ preferences, outperforming a pretrained UI evaluator and a larger multimodal model offline and outperforming all baselines in an online study with 12 new users. "whyItMatters":"The study demonstrates a practical approach to personalizing on‑demand interfaces, showing that even sparse, subjective feedback can be effectively used to improve user satisfaction with generative UI designs."
Hugging Face Trending Papers
Aug 26
VGA-BenchV2: An Expanded Unified Benchmark and Multi-Model Framework for Evaluating Video Aesthetics and Generation Quality
We introduce VGA-BenchV2, an extended human-aligned benchmark and optimization framework for jointly evaluating and improving video generation quality and aesthetic value. Built upon VGA-Bench, VGA-Be...
Hugging Face Trending Papers
Aug 13
P2Fusion: Prompt-based Progressive Infrared-Visible Image Fusion via Dual-Prior Distillation
Infrared-visible image fusion (IVIF) is pivotal for multimodal perception, yet reconciling the inherent information disparity between thermal and textural features remains a fundamental challenge. Existing prior-guided methods often rely on static constraints that induce optimization conflicts or utilize extrinsic semantic priors from large-scale foundation models (e.
arXiv Computer Vision
Aug 27
VGA-BenchV2: An Expanded Unified Benchmark and Multi-Model Framework for Evaluating Video Aesthetics and Generation Quality
VGA‑BenchV2 is an expanded, human‑aligned benchmark and optimization framework that jointly evaluates video generation quality and aesthetic value. It builds on the original VGA‑Bench taxonomy, adding 52 sub‑dimensions and 1,016 curated prompts to generate over 60,000 videos from 12 mainstream models. The benchmark significantly enlarges human supervision with 36,000 task‑level annotations and introduces a hybrid evaluator (VAQA‑Net, VTag‑Net, VGQA‑Net) that aligns well with human judgments and can be used as a reward model for reinforcement‑learning fine‑tuning.
arXiv Computer Vision
2d ago
Beyond Domain-Level Adaptation: Margin-Oriented Semantic-Appearance Interaction Correction for Personalized Federated Vision-Language Models
arXiv:2610.01625v1 Announce Type: new Abstract: Federated parameter-efficient fine-tuning enables distributed clients to adapt pretrained vision-language models without sharing raw data or updating t...