ShowTable：通过协同反思与精炼解锁创意表格可视化
ShowTable: Unlocking Creative Table Visualization with Collaborative Reflection and Refinement
把 MLLM 当作规划与错误评判的编排器、扩散模型当作执行器，通过渐进式自纠正循环把结构化数据映射为高保真信息图，这一「推理-生成-纠错」闭环可迁移到任何需要精确数据到视觉映射的条件生成任务。
原文依据 we propose ShowTable, a pipeline that synergizes MLLMs with diffusion models via a progressive self-correcting process
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论现有生成模型与统一模型在通用图像生成上表现优异，但在需要深度推理、规划以及超越一般场景的精确数据到视觉映射能力的任务上仍显不足。为突破现有局限，我们引入一项全新且具有挑战性的任务：创意表格可视化，要求模型生成一幅信息图，忠实且美观地将给定表格中的数据可视化。为应对该挑战，我们提出 ShowTable，一条通过渐进式自纠正过程将多模态大语言模型（MLLM）与扩散模型协同起来的流水线。MLLM 充当核心编排器，负责推理视觉方案并评判视觉错误以给出精炼指令，扩散模型执行 MLLM 的指令，从而获得高保真结果。为支撑该任务与我们的流水线，我们引入三条自动化数据构建流水线，用于训练不同模块。此外，我们提出 TableVisBench，一个包含 800 个挑战性实例、覆盖 5 个评估维度的新基准，用以评估该任务上的表现。实验表明，我们的流水线在搭配不同模型实例化时均显著优于基线，凸显了其有效的多模态推理、生成与纠错能力。
英文原文摘要 1298 字符
While existing generation and unified models excel at general image generation, they struggle with tasks requiring deep reasoning, planning, and precise data-to-visual mapping abilities beyond general scenarios. To push beyond the existing limitations, we introduce a new and challenging task: creative table visualization, requiring the model to generate an infographic that faithfully and aesthetically visualizes the data from a given table. To address this challenge, we propose ShowTable, a pipeline that synergizes MLLMs with diffusion models via a progressive self-correcting process. The MLLM acts as the central orchestrator for reasoning the visual plan and judging visual errors to provide refined instructions, the diffusion execute the commands from MLLM, achieving high-fidelity results. To support this task and our pipeline, we introduce three automated data construction pipelines for training different modules. Furthermore, we introduce TableVisBench, a new benchmark with 800 challenging instances across 5 evaluation dimensions, to assess performance on this task. Experiments demonstrate that our pipeline, instantiated with different models, significantly outperforms baselines, highlighting its effective multi-modal reasoning, generation, and error correction capabilities.