VIEScore2: Unified Image Evaluation with Spatially Grounded Explanations

Hugging Face Daily Papers Papers
image-evaluation defect-localization spatial-grounding multimodal-evaluator grpo vlm image-generation

Summary

VIEScore2 introduces a unified evaluator for image generation and editing that predicts quality scores and defect locations on an NxN grid in a single model pass, using GRPO post-training with cell-level localization rewards. It outperforms general-purpose VLMs such as Gemini-3-Flash, achieving 0.601 overall SRCC and top-three results on five of six defect-localization benchmarks.

Existing synthetic image evaluators typically provide only a scalar quality score and do not identify the image regions that support it. We introduce VIEScore2, a unified evaluator for image generation and editing tasks with optional conditioning images. VIEScore2 represents an image as an N x N grid and jointly predicts quality scores and defect locations in a single model pass. Its text-native grid representation provides a common interface for heterogeneous spatial supervision and enables directly verifiable post-training objectives. We train on 38K examples spanning score-only, localization-only, and joint supervision across generation and editing tasks. Starting from supervised fine-tuning, we further apply GRPO to improve defect localization using rewards that combine cell-level Dice overlap, score accuracy, and output-format validity. A parameter-free parser converts the structured predictions into readable explanations. On the primary suite, VIEScore2 achieves an overall-score SRCC of 0.601, compared with 0.491 for Gemini-3-Flash, the strongest zero-shot general-purpose VLM baseline under matched inputs. For defect localization, VIEScore2 outperforms both general-purpose VLMs and specialized spatial evaluators on three of six benchmarks in per-image grid IoU and ranks among the top three on five, including datasets beyond its training sources.
Original Article
View Cached Full Text

Cached at: 10/08/26, 12:24 PM

Paper page - VIEScore2: Unified Image Evaluation with Spatially Grounded Explanations

Source: https://huggingface.co/papers/2610.00994 Published on Oct 1

·

Submitted byhttps://huggingface.co/vinesmsuic

Max Kuon Oct 8

Abstract

Existingsyntheticimageevaluatorstypicallyprovideonlyascalarqualityscoreanddonotidentifytheimageregionsthatsupportit.WeintroduceVIEScore2,aunifiedevaluatorforimagegenerationandeditingtaskswithoptionalconditioningimages.VIEScore2representsanimageasanNxNgridandjointlypredictsqualityscoresanddefectlocationsinasinglemodelpass.Itstext-nativegridrepresentationprovidesacommoninterfaceforheterogeneousspatialsupervisionandenablesdirectlyverifiablepost-trainingobjectives.Wetrainon38Kexamplesspanningscore-only,localization-only,andjointsupervisionacrossgenerationandeditingtasks.Startingfromsupervisedfine-tuning,wefurtherapplyGRPOtoimprovedefectlocalizationusingrewardsthatcombinecell-levelDiceoverlap,scoreaccuracy,andoutput-formatvalidity.Aparameter-freeparserconvertsthestructuredpredictionsintoreadableexplanations.Ontheprimarysuite,VIEScore2achievesanoverall-scoreSRCCof0.601,comparedwith0.491forGemini-3-Flash,thestrongestzero-shotgeneral-purposeVLMbaselineundermatchedinputs.Fordefectlocalization,VIEScore2outperformsbothgeneral-purposeVLMsandspecializedspatialevaluatorsonthreeofsixbenchmarksinper-imagegridIoUandranksamongthetopthreeonfive,includingdatasetsbeyonditstrainingsources.

View arXiv pageView PDFProject pageGitHub1Add to collection

Get this paper in your agent:

hf papers read 2610\.00994

Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash

Models citing this paper1

#### Allenda/VIEScore2 Image-Text-to-Text• 9B• Updatedabout 10 hours ago • 47 • 1

Datasets citing this paper0

No dataset linking this paper

Cite arxiv.org/abs/2610.00994 in a dataset README.md to link it from this page.

Spaces citing this paper1

Collections including this paper0

No Collection including this paper

Add this paper to acollectionto link it from this page.

Similar Articles

VISTA: View-Consistent Self-Verified Training for GUI Grounding

Hugging Face Daily Papers

VISTA introduces a view-consistent self-verified training method for GUI grounding that improves GRPO-based coordinate prediction by using multiple target-preserving views, achieving consistent accuracy gains across benchmarks.

GST-Bench: Can VLMs Develop Global Spatial Awareness from Video?

Hugging Face Daily Papers

GST-Bench is a new VQA benchmark for evaluating global spatial awareness in video understanding, testing whether VLMs can build coherent global scene representations from long-horizon egocentric video. Evaluation of 22 state-of-the-art VLMs shows a large gap versus humans, with the best model scoring 42.68 vs 79.08.

SGOCR: A Spatially-Grounded OCR-focused Pipeline & V1 Dataset [P]

Reddit r/MachineLearning

SGOCR is an open-source dataset pipeline for generating spatially-grounded, OCR-focused visual question answering (VQA) tuples with rich metadata to support diverse VLM training. The pipeline uses a multi-stage approach combining models like Nvidia's nemotron-ocr-v2, Gemma4, Qwen3-VL, and Gemini-2.5-Flash, along with an agentic optimization loop.