SVG-Score introduces a human‑aligned evaluation framework for text‑to‑SVG generation, addressing the shortcomings of existing image‑based metrics like CLIPScore that poorly capture SVG‑specific errors such as color, count, and spatial inaccuracies. The authors first demonstrate that CLIP‑based scores are largely insensitive to these errors and that generic Vision‑Language Models respond inconsistently across error types and styles. They then present a human‑annotated Semantic Alignment dataset and develop two complementary evaluators: a CLIP‑based scorer adapted to vector graphics and a VLM judge refined through supervised fine‑tuning and reward‑shaped reinforcement learning, enabling both fast large‑scale and expressive, interpretable assessment of SVG generators.
By Marco Cipriano, Leonardo Zini, Alexandra Schild, Valentin Teutschbein, Afsana Mimi, Marcella Cornia, Lorenzo Baraldi, Gerard de Melo
arXiv:2603.29852v2 Announce Type: replace-cross
Abstract: We introduce VectorGym, a comprehensive benchmark suite for Scalable Vector Graphics (SVG) that spans generation from text and sketches, comp...
By Joan Rodriguez, Haotian Zhang, Abhay Puri, Haoran Dai, Tianyang Zhang, Meng Lin, Rishav Pramanik, Xiaoqing Xie, Marco Terral Rodriguez, Darsh Kaushik, Aly Shariff, Perouz Taslakian, Spandana Gella, Sai Rajeswar, David Vazquez, Christopher Pal, Marco Pedersoli
arXiv:2606.29997v2 Announce Type: replace
Abstract: Automatic evaluation of image and video captioning is essential for benchmarking multimodal systems, although standard evaluation metrics show limi...
By Shuitsu Koyama, Kazuki Matsuda, Yuiga Wada, Shinnosuke Hirano, Daichi Yashima, Komei Sugiura
Automatic evaluation of image and video captioning is essential for benchmarking multimodal systems, although standard evaluation metrics show limited alignment with human judgments. Recent approaches using large language models (LLMs), commonly referred to as LLM-as-a-Judge, have improved alignment with human judgments but still suffer from a mismatch between large-vocabulary language modeling and evaluation over a small label set.
We introduce VGA-BenchV2, an extended human-aligned benchmark and optimization framework for jointly evaluating and improving video generation quality and aesthetic value. Built upon VGA-Bench, VGA-Be...
arXiv:2607. 15740v1 Announce Type: cross Abstract: As Text-to-Image (T2I) systems rapidly advance, evaluating the cultural authenticity of synthesized content has become increasingly important for fair and trustworthy generative AI.
By Bo-An Chang, Yu-Chih Chen