Signals 4 · free daily AI digest

MetaRefine: Score Refinement with Multi-Level Features for Image Assessment

Paper recorded by Signals 4 on 2026-10-29 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-29 on arXiv · recorded by Signals 4 on 2026-10-30

Category: cs.CV · 计算机视觉 · first seen 2026-10-30

Abstract

An image's original dimensions, aspect ratio, and composition provide important cues that can affect its technical and aesthetic quality. Image assessment methods use resizing, cropping, padding, or patch sampling to accommodate different input sizes. However, these operations can alter or obscure original geometric and compositional cues. Existing assessment models also encode rich multi-level vi

Read on arXiv →

#61 most recent of 710 cs.CV papers we have recorded · ↑ newer: LucidLyra: Feed-Forward Generative 3D Scene Reconstruction and Joint U · ↓ older: AVPS-360: A Spatial Audio-Visual Benchmark for Instance Segmentation a
Cite this page: MetaRefine: Score Refinement with Multi-Level Features for Image Assessment: the #61 most recent of 710 cs.CV papers we have recorded (as of 2026-10-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/metarefine-score-refinement-with-multi-level-features-for-image-assessment.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.CV papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions