Significant progress has been made in the field of Instruction-based Image Editing Models (IIEMs). However, while these models demonstrate plausible adherence to instructions and strong reasoning ability on current benchmarks, their ability to edit small objects remains underexplored, despite its importance for precise local editing and refining details in both real and generated images. In this paper, we introduce DeepLookEditBench (DLEBench), the first benchmark dedicated to assessing the abilities of IIEMs in editing small-scale objects. Specifically, we construct a challenging testbed comprising 1889 samples across seven instruction types. In these samples, target objects occupy only 1%-10% of the image area, covering complex scenarios such as partial occlusion and multi-object editing. To ensure robust evaluation on this benchmark, we propose an evaluation protocol with refined score rubrics to minimize subjectivity and ambiguity in two criteria: Instruction Following and Visual Consistency. This protocol also introduces a dual-mode evaluation framework (Tool-driven and Oracle-guided Modes) addressing the misalignment between LMM-as-a-Judge and human judgements on DLEBench. Empirical results on 10 IIEMs reveal significant performance gaps in small-scale object editing, highlighting the need for specialized benchmarks to advance this ability.
FreeEdit: Mask-free Reference-based Image Editing with Multi-modal Instruction
Chinese Academy of Sciences Institute of Information Engineering（中国科学院信息工程研究所）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；Meituan（美团）
East China Normal University（华东师范大学）；Xiaohongshu Inc.（小红书）；KLATASDS, MOE, China（中国教育部统计与数据科学重点实验室）；The Chinese University of Hong Kong（香港中文大学）；Zhejiang University（浙江大学）；Fudan University（复旦大学）；University of Oxford（牛津大学）；Shanghai Jiao Tong University（上海交通大学）；University of Science and Technology of China（中国科学技术大学）；Nanjing University（南京大学）
Tuning-free Instruction-based Video Editing Via Structural Noise Initialization and Guidance
JIUTIAN Research, China Mobile（中国移动极天研究院）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）；State Key Laboratory of Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）
Beyond Editing Pairs: Fine-Grained Instructional Image Editing via Multi-Scale Learnable Regions
Department of Electronic Engineering and Computer Science University of California, Irvine（电子工程与计算机科学系 加州大学尔湾分校）；Department of Computer Science University of Alabama at Birmingham（计算机科学系 阿拉巴马大学伯明翰分校）
