arXiv Computer Vision
ChartJudgeBench: Evaluating LMM Judges for Chart-to-Code Generation
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arXiv AI
3d ago
From Charts to Code: A Hierarchical Benchmark for Multimodal Models
arXiv:2510.17932v5 Announce Type: replace-cross Abstract: We introduce Chart2Code, a new benchmark for evaluating the chart understanding and code generation capabilities of large multimodal models (...
arXiv AI
Sep 15
ChartAnno: Benchmarking Multimodal Large Language Models for Chart Annotation Generation
arXiv:2608.03464v2 Announce Type: replace Abstract: Annotations are essential to communicative visualization, helping explain data, emphasize key findings, and guide attention. While multimodal large...
Hugging Face Trending Papers
Aug 11
VisEditBench: Can Vision-Language Models Edit Visualization Code from Multimodal Feedback?
Vision-language models (VLMs) have shown strong capabilities in generating visualization code from textual or visual specifications. However, real-world visualization authoring is inherently iterative: users frequently revise existing visualizations to repair flawed charts or adapt them to desired styles.
arXiv AI
Sep 1
LOCI: A Locator-Critic with Refinement Loop
LOC I (Locator‑Critic) is a training‑free framework that separates visual search from evidence verification in Vision‑Language Models. It uses a Locator agent to propose candidate visual evidence and a Critic agent to assess its relevance, engaging in an iterative refinement loop that progressively improves the evidence until it is sufficient to answer a question. The approach yields state‑of‑the‑art results on several complex visual benchmarks, boosting accuracy for both open‑weight models like Qwen3‑VL and proprietary models such as Gemini 2.5 Pro.
arXiv Computation and Language
Sep 1
Beyond NL2Code: A Structured Survey of Multimodal Code Intelligence
The paper surveys Multimodal Code Intelligence, focusing on tasks where code is generated, edited, refined, or reasoned about under visually grounded inputs such as screenshots, charts, and videos. It categorizes the field by the role of code—rendered artifact, editable structure, intermediate reasoning trace, or executable tool interface—and organizes benchmarks into four domains: Graphical User Interface, Scientific Visualization, Structured Graphics, and Frontier Tasks and Frameworks. The authors argue that reliable evaluation must include evidence of semantics and interaction beyond visual fidelity, and propose four verification-centered research directions to advance the field toward evidence-grounded executable systems.
arXiv Computer Vision
Aug 25
FIRM-Video: Check Before You Score for Reliable Text-to-Video Reward Modeling
arXiv:2608.21839v1 Announce Type: new Abstract: Reliable reward models are essential for text-to-video evaluation and alignment. However, the trade-off between evaluation accuracy and inference effic...