ChartLens: A Dual-Branch Framework for Chart Data Correction and Factual Summary Refinement
In this report, we present our champion solution for the DataMFM Challenge Track 2: Chart Understanding. This track requires models to recover structured chart data and generate faithful natural-language summaries from chart images. To address the complementary requirements of accurate data extraction and factual narration, we propose ChartLens, a dual-branch framework for chart data correction and summary refinement. ChartLens consists of two key modules: Structure-Aware CSV Verification and Correction (SAVC) and Text-Retention-Guided Summary Refinement (TRSR). SAVC improves the reliability of structured data extraction through verification and correction, while TRSR enhances summary generation by preserving critical textual and numerical evidence from charts. By combining model adaptation, correction-based generation, and OCR-assisted evidence grounding, ChartLens improves both structured data recovery and summary factuality. On the test set, our final system achieves an overall score of 69.10 and ranks first in Track 2, demonstrating its effectiveness for accurate chart understanding. Our code will be released at: https://github.com/iLearn-Lab/CVPRW26-ChartLens.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
ChartLens: Fine-grained Visual Attribution in Charts
The growing capabilities of multimodal large language models (MLLMs) have advanced tasks like chart understanding. However, these models often suffer from hallucinations, where generated text sequences conflict with the …
Chart UnderstandingRSD: Moving Local Triangular Charts for Auditing Language-Model Hidden States
We study Relational Semantic Decomposition, abbreviated as RSD, as a moving local triangular chart audit for language-model hidden states. For repeated occurrences of one target word, RSD fits a shared three-anchor membe…
Enhancing Chart-to-Code Generation in Multimodal Large Language Models via Iterative Dual Preference Learning
Chart-to-code generation, the process of converting chart images into executable plotting scripts, provides a lossless representation of chart information, requiring models to accurately capture and summarize all visual …
Code GenerationArrow-Guided VLM: Enhancing Flowchart Understanding via Arrow Direction Encoding
Flowcharts are indispensable tools in software design and business-process analysis, yet current vision-language models (VLMs) frequently misinterpret the directional arrows and graph topology that set these diagrams apa…
Optical Character RecognitionOptical Character Recognition (OCR)Navigating the Mirage: A Dual-Path Agentic Framework for Robust Misleading Chart Question Answering
Despite the success of Vision-Language Models (VLMs), misleading charts remain a significant challenge due to their deceptive visual structures and distorted data representations. We present ChartCynics, an agentic dual-…
Chart Question Answering