arXiv AI
Jun 16
Tool-IQA: Augmenting Image Quality Assessment with Simple Tools
arXiv:2606. 16082v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have been increasingly adopted for Image Quality Assessment (IQA).
MetaRefine: Score Refinement with Multi-Level Features for Image Assessment
MetaRefine is a lightweight, metadata‑aware score refinement architecture that uses original image geometry, multi‑level visual features, and a frozen model’s initial score to predict corrections. It improves correlation metrics (PLCC and SRCC) and reduces error metrics (MAE and RMSE) across 100 configurations on 21 datasets, with average gains of 7.55% and 7.10% and reductions of 23.27% and 21.93%, respectively. The method adds only a median 1.11% parameter overhead and can enhance state‑of‑the‑art assessment models on multiple benchmarks.
Related stories
arXiv Computer Vision
Oct 2
VIEScore2: Unified Image Evaluation with Spatially Grounded Explanations
arXiv:2610.00994v1 Announce Type: new Abstract: Existing synthetic image evaluators typically provide only a scalar quality score and do not identify the image regions that support it. We introduce V...
arXiv AI
Aug 18
MetaReason: Precise Interleaved Multimodal Reasoning via Editing Meta Information for Solving Geometry Problems
arXiv:2608. 15006v1 Announce Type: cross Abstract: Although visual reasoning is crucial for solving complex geometry tasks, existing vision-language models rely heavily on text-only reasoning.
arXiv Computer Vision
Oct 2
Discrete Annotation, Continuous Preference: Rethinking Supervision for Accurate and Generalizable Aesthetic Image Cropping
The paper introduces a new approach to aesthetic image cropping by modeling human preference as a continuous, multi-peaked field rather than relying on discrete, grid‑based annotations. It presents the Continuous Preference Field (CPF) that reconstructs a dense preference landscape from sparse labels, and uses this to train a VLM‑based cropping model (CPIC) that achieves state‑of‑the‑art accuracy and strong out‑of‑domain generalization. Additionally, the authors propose CPICD, a recalibrated benchmark that corrects grid‑bound artifacts in existing datasets, providing a more reliable evaluation framework.
arXiv Machine Learning
Oct 12
Vision2Code: A Multi-Domain Benchmark for Evaluating Image-to-Code Generation
Vision2Code is a new benchmark for image-to-code generation that covers 15 domains and 539 test examples without relying on reference source code. It evaluates generated programs by executing them and comparing rendered outputs to the original images using domain‑specific rubrics, yielding scores that correlate better with human judgments than generic metrics. The study shows domain variability in model performance and demonstrates that the scoring method can improve fine‑tuning of models like Qwen3.5‑9B and Phi‑4‑MM via rejection sampling.
arXiv Computer Vision
5d ago
From Evaluation to Evolution: Benchmarking Image Editing and Evolving Image Models
The paper introduces WeGenEditBench, a benchmark with 2,740 evaluation instances and 148 task types for image editing, focusing on local failure detection and prioritization for model improvement. It also presents WeGenEvoStudio, a multi‑agent harness that supports recursive self‑improvement by diagnosing failures, constructing retrieval‑grounded data, and adapting models. Experiments show that models such as GPT‑Image‑2 and Seedream‑5.0‑Pro excel in single‑image and text‑intensive editing, and evolution experiments demonstrate significant targeted gains on selected benchmarks.
arXiv Computer Vision
Oct 27
ScenePercept: Scenario-Conditioned Perceptual Assessment of Real-World User-Generated Content Images
arXiv:2610.17661v2 Announce Type: replace Abstract: The rapid development of mobile imaging and social platforms has led to explosive growth in user-generated content images, making their systematic...
arXiv Computer Vision
Oct 26
MDMR-Bench: A Multi-Dimensional Benchmark for Multi-Reference Image Generation
arXiv:2610.32459v1 Announce Type: new Abstract: Image generation models have rapidly advanced, with many models now supporting generation conditioned on multiple reference images and textual instruct...
arXiv AI
Aug 10
SABRE: Scalable and Automated Benchmarking of VLMs under Stress
arXiv:2608. 07435v1 Announce Type: cross Abstract: Vision-language models (VLMs) are improving rapidly, but benchmark development lags behind, making weaknesses hard to identify.
arXiv Machine Learning
2d ago
EPIC: Efficient Predicate-Guided Inference-Time Control for Compositional Text-to-Image Generation
arXiv:2605.11722v2 Announce Type: replace-cross Abstract: Recent text-to-image (T2I) generators can synthesize realistic images, but still struggle with compositional prompts involving multiple objec...
Hugging Face Trending Papers
Aug 3
MIEScore: Human-Aligned Evaluation for Multi-Source Image Editing
Recent advances in unified multimodal models have significantly improved text-guided image editing abilities. In particular, models such as Nano-Banana-Pro and GPT-Image-2 demonstrate emerging capabilities in multi-source image editing (MIE), including tasks such as object synthesis, person-background composition, and cross-image style fusion.
arXiv Computer Vision
Oct 2
SR-Ground: Image Quality Grounding for Super-Resolved Content
SR‑Ground is a large‑scale dataset created to enable fine‑grained segmentation of visual artifacts in super‑resolved images. It contains 63,000 images processed by various state‑of‑the‑art SR models, each annotated at the pixel level for six distinct artifact types, validated through a crowdsourcing study with 1,062 participants. The dataset improves the training of image quality assessment models with grounding capabilities and supports a fine‑tuning pipeline that reduces perceptible artifacts in SR outputs, outperforming no‑reference methods on both benchmark and real‑world low‑resolution datasets.