Repository contents
Naming convention: VIEScore2 denotes the full model after GRPO;
ablated variants are marked by what they lack, never by added suffixes.
Table with columns: path, contents| path | contents |
|---|
/ (root) | VIEScore2 — the full post-GRPO checkpoint (drop-in Qwen3VLForConditionalGeneration) |
wo-grpo/ | VIEScore2 (w/o GRPO) — the SFT-stage LoRA adapter on Qwen/Qwen3-VL-8B-Instruct |
Usage
from transformers import AutoProcessor, Qwen3VLForConditionalGeneration model = Qwen3VLForConditionalGeneration.from_pretrained( "Allenda/VIEScore2", dtype="bfloat16", device_map="auto")processor = AutoProcessor.from_pretrained("Allenda/VIEScore2")
Evaluation prompts, the frozen protocol, the deterministic explanation
renderer, and all benchmark converters and baseline harnesses live in the
accompanying code release (see the paper).
Evaluation
On a 1,300-example multi-source suite (RichHF, PAL4VST, EvalMuse,
ImagenWorld, COCO; frozen protocol v3.1): localization cell-F1 0.506,
problem-sample grid IoU 0.324, overall-score SRCC 0.601. After mapping
all methods to a shared 16×16 grid, VIEScore2 is the only evaluator in the
top two of cell-F1 or grid-IoU on all six external localization benchmarks
(RichHF, AbHuman, HAD, SynthScars, PAL4VST, SDG-30K). See the paper and code
repository for the full tables and protocol details.
Citation
@misc{du2026viescore2unifiedimageevaluation, 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}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2610.00994}, }