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From the 3 of 20 linked papers with an AI index.

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20 papers

cs.CV2026

Learning Color Grading, No Photo Sharing: Federated Aesthetic Preference Learning for Personalized Image Enhancement

Chuanzhi Xu, Ziyuan Tao, Jean Julien KNell +5

The paper proposes FedPAIE, a federated learning framework that learns individual aesthetic preferences for color grading and applies lightweight, personalized image enhancement on…

cs.CV2026

SciFigQual-Bench: A Benchmark for Scientific Figure Quality Assessment with Full-Manuscript Context

Zihan Deng, Chuanzhi Xu, Huiqi Liang +3

The paper introduces SciFigQual-Bench, a benchmark dataset that evaluates the quality of scientific figures within full manuscript context across five dimensions, and presents a cr…

cs.CV2026

SciFigAlign: Scoring Scientific Figures by Fine-tuned Alignment of Visuals with Manuscript Evidence

Chuanzhi Xu, Zihan Deng, Huiqi Liang +4

The paper introduces SciFigAlign, a multimodal model that scores scientific figures by aligning visual content with manuscript evidence, using fine‑tuned CLIP and SciBERT to predic…

cs.CV2026

MGDFIS: Multi-scale Global-detail Feature Integration Strategy for Small Object Detection

Yuxiang Wang, Xuecheng Bai, Chuanzhi Xu +2

Small-object detection in Unmanned Aerial Vehicle (UAV) imagery requires preserving weak local evidence while using broader context to separate tiny foreground targets from clutter…

cs.CV2026

EvLIR: Learning Illumination Residuals from Ordered Events for Low-Light Image Enhancement

Haoxian Zhou, Chuanzhi Xu, Langyi Chen +5

Low-light image enhancement is severely ill-posed when the input frame contains missing structure, saturated noise, and weak local contrast. Event cameras provide asynchronous brig…

cs.CV2026

SAFE-DiT: Semantics-Aware Fast-path Execution for High-Resolution Diffusion Transformers

Xuanhua Yin, Yuxuan Jia, Chuanzhi Xu +1

High-resolution Diffusion Transformer (DiT) inference contains substantial spatial redundancy, but many spatially adaptive implementations encode regional computation as attention…