The increasing popularity of long Text-to-Image (T2I) generation has created an urgent need for automatic and interpretable models that can evaluate the image-text alignment in long prompt scenarios. However, the existing T2I alignment benchmarks predominantly focus on short prompt scenarios and only provide MOS or Likert scale annotations. This inherent limitation hinders the development of long T2I evaluators, particularly in terms of the interpretability of alignment. In this study, we contribute LongT2IBench, which comprises 14K long text-image pairs accompanied by graph-structured human annotations. Given the detail-intensive nature of long prompts, we first design a Generate-Refine-Qualify annotation protocol to convert them into textual graph structures that encompass entities, attributes, and relations. Through this transformation, fine-grained alignment annotations are achieved based on these granular elements. Finally, the graph-structed annotations are converted into alignment scores and interpretations to facilitate the design of T2I evaluation models. Based on LongT2IBench, we further propose LongT2IExpert, a LongT2I evaluator that enables multi-modal large language models (MLLMs) to provide both quantitative scores and structured interpretations through an instruction-tuning process with Hierarchical Alignment Chain-of-Thought (CoT). Extensive experiments and comparisons demonstrate the superiority of the proposed LongT2IExpert in alignment evaluation and interpretation. Data and code have been released in https://welldky.github.io/LongT2IBench-Homepage/.
Structured Prediction with Output Embeddings for Semantic Image Annotation
Xerox Research Europe（施乐欧洲研究中心）；Institut de Robòtica i Informàtica Industrial, CSIC-UPC（西班牙国家研究委员会-加泰罗尼亚理工大学工业机器人与信息技术研究所）；TALP Research Center, Universitat Politècnica de Catalunya（加泰罗尼亚理工大学TALP研究中心）
Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling
Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与成像工程技术研究中心）；ShanghaiTech University（上海科技大学）；The Pennsylvania State University（宾夕法尼亚州立大学）
Evaluating and Correcting Human Annotation Bias in Dynamic Micro-Expression Recognition
School of Psychology, Shanghai Jiao Tong University（心理学系，上海交通大学）；School of Artificial Intelligence and Computer Science, Jiangnan University（人工智能与计算机科学学院，江南大学）
Evaluating the Interpretability of Sparse Autoencoders with Concept Annotations
The Berlin Institute for the Foundations of Learning and Data (BIFOLD)（柏林学习与数据基础研究所）；Technische Universität Berlin（柏林工业大学）；INRAE（法国国家农业、食品与环境研究院）；Inria, EVERGREEN（法国国家信息与自动化研究所，EVERGREEN）；UMR TETIS, Univ Montpellier（UMR TETIS，蒙彼利埃大学）
From Drawings to Decisions: A Hybrid Vision-Language Framework for Parsing 2D Engineering Drawings into Structured Manufacturing Knowledge
Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research (A*STAR)（新加坡制造技术研究院，新加坡科技研究局）；Advanced Remanufacturing and Technology Centre (ARTC), Agency for Science, Technology and Research (A*STAR)（先进再制造与技术中心，新加坡科技研究局）；Nanyang Technological University（南洋理工大学）
