| title | VIEScore2: Unified Image Evaluation with Spatially Grounded Explanations |
|---|---|
| authors | Xianda Du, Max Ku, Weiming Ren, Zhi Rui Tam, Chunlin Ren, Ping Nie, Min-Hung Chen, Wenhu Chen |
| date | 2026-10-01 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2610.00994 |
| affiliation | TIGER-Lab |
| upvotes | 19 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | 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. |
cite
@misc{du2026viescore,
title={VIEScore2: Unified Image Evaluation with Spatially Grounded Explanations},
author={Xianda Du and Max Ku and Weiming Ren and Zhi Rui Tam and Chunlin Ren and Ping Nie and Min-Hung Chen and Wenhu Chen},
year={2026},
eprint={2610.00994},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.00994}
}