KRIS-Bench：下一代智能图像编辑模型的基准测试
KRIS-Bench: Benchmarking Next-Level Intelligent Image Editing Models
AI总结：
KRIS-Bench提出一个基于知识类型分类法的图像编辑推理基准，含22个任务和1,267个实例，通过知识合理性度量评估10个模型，揭示推理性能差距。
摘要：
多模态生成模型的最新进展已推动基于指令的图像编辑取得显著进步。然而，尽管这些模型能产生视觉上合理的输出，其基于知识的推理编辑能力仍未得到充分探索。本文中，我们提出KRIS-Bench（图像编辑系统中的知识推理基准），这是一个通过认知信息视角评估模型的诊断性基准。借鉴教育理论，KRIS-Bench将编辑任务划分为三种基础知识类型：事实性、概念性和程序性。基于此分类法，我们设计了涵盖7个推理维度的22个代表性任务，并发布了1,267个高质量标注的编辑实例。为支持细粒度评估，我们提出了一套综合协议，其中包含一种新颖的知识合理性度量，该度量通过知识提示增强，并通过人类研究进行校准。对10个最先进模型的实证结果揭示了推理性能上的显著差距，凸显了以知识为中心的基准对于推进智能图像编辑系统发展的必要性。
英文摘要：
Recent advances in multi-modal generative models have enabled significant progress in instruction-based image editing. However, while these models produce visually plausible outputs, their capacity for knowledge-based reasoning editing tasks remains under-explored. In this paper, we introduce KRIS-Bench (Knowledge-based Reasoning in Image-editing Systems Benchmark), a diagnostic benchmark designed to assess models through a cognitively informed lens. Drawing from educational theory, KRIS-Bench categorizes editing tasks across three foundational knowledge types: Factual, Conceptual, and Procedural. Based on this taxonomy, we design 22 representative tasks spanning 7 reasoning dimensions and release 1,267 high-quality annotated editing instances. To support fine-grained evaluation, we propose a comprehensive protocol that incorporates a novel Knowledge Plausibility metric, enhanced by knowledge hints and calibrated through human studies. Empirical results on 10 state-of-the-art models reveal significant gaps in reasoning performance, highlighting the need for knowledge-centric benchmarks to advance the development of intelligent image editing systems.
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