Video editing models have advanced significantly, but evaluating their performance remains challenging. Traditional metrics, such as CLIP text and image scores, often fall short: text scores are limited by inadequate training data and hierarchical dependencies, while image scores fail to assess temporal consistency. We present SST-EM (Semantic, Spatial, and Temporal Evaluation Metric), a novel evaluation framework that leverages modern Vision-Language Models (VLMs), Object Detection, and Temporal Consistency checks. SST-EM comprises four components: (1) semantic extraction from frames using a VLM, (2) primary object tracking with Object Detection, (3) focused object refinement via an LLM agent, and (4) temporal consistency assessment using a Vision Transformer (ViT). These components are integrated into a unified metric with weights derived from human evaluations and regression analysis. The name SST-EM reflects its focus on Semantic, Spatial, and Temporal aspects of video evaluation. SST-EM provides a comprehensive evaluation of semantic fidelity and temporal smoothness in video editing. The source code is available in the \textbf{\href{https://github.com/custommetrics-sst/SST_CustomEvaluationMetrics.git}{GitHub Repository}}.
FiVE: A Fine-grained Video Editing Benchmark for Evaluating Emerging Diffusion and Rectified Flow Models
Harvard AI and Robotics Lab, Harvard University（哈佛人工智能与机器人实验室，哈佛大学）；Broad Institute（博德研究所）；Hong Kong Polytechnic University（香港理工大学）；School of Engineering and Applied Sciences, Harvard University（哈佛大学工程与应用科学学院）；City University of Hong Kong（香港城市大学）；Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University（自然与人工智能研究学院，哈佛大学）
LMM4Edit: Benchmarking and Evaluating Multimodal Image Editing with LMMs
Institute of Image Communication and Network Engineering, Shanghai JiaoTong University（上海交通大学图像通信与网络工程研究所）；University of Electronic and Science Technology of China（电子科技大学）；Nanyang Technological University（南洋理工大学）
ChartEdit: How Far Are MLLMs From Automating Chart Analysis? Evaluating MLLMs' Capability via Chart Editing
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Harbin Institute of Technology（哈尔滨工业大学）；Tianjin University（天津大学）；Tsinghua University（清华大学）
Reasoning to Edit: Hypothetical Instruction-Based Image Editing with Visual Reasoning
Tencent Youtu Lab（腾讯云图实验室）；Sichuan University（四川大学）；University of the Chinese Academy of Sciences（中国科学院大学）；Fudan University（复旦大学）；Zhejiang University（浙江大学）；National University of Singapore（新加坡国立大学）；Nanyang Technological University（南洋理工大学）
Human-Aligned MLLM Judges for Fine-Grained Image Editing Evaluation: A Benchmark, Framework, and Analysis
University of Virginia（弗吉尼亚大学）；Columbia University（哥伦比亚大学）；Vanderbilt University（范德比大学）；Adobe Research（Adobe研究）；Dolby Laboratories（杜比实验室）；Cisco Research（思科研究）；University of Southern California（南加州大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；University of Oregon（俄勒冈大学）；Texas A&M University（德克萨斯大学）
DICE: Discrete Inversion Enabling Controllable Editing for Multinomial Diffusion and Masked Generative Models
Rutgers University（新泽西罗格斯大学）；MIT-IBM Watson AI Lab（MIT-IBM沃森人工智能实验室）；Red Hat AI Innovation（红帽AI创新）；Google DeepMind（谷歌DeepMind）；NYU（纽约大学）；Walmart Global Tech（沃尔玛全球技术）；NEC Labs America（NEC美国实验室）；Massachusetts Institute of Technology（麻省理工学院）；ANU（澳大利亚国立大学）；UT Arlington（德克萨斯大学阿灵顿分校）
PICABench: How Far Are We from Physically Realistic Image Editing?
Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）；CUHK MMLab（香港中文大学多媒体实验室）；Krea AI；Beihang University（北京航空航天大学）；Tongyi Lab（通义实验室）；USTC（中国科学技术大学）；The University of Hong Kong（香港大学）
EditHF-1M: A Million-Scale Rich Human Preference Feedback for Image Editing
Shanghai Jiao Tong University（上海交通大学）；Vivo Mobile Communication Co., Ltd（vivo移动通信有限公司）；University of Electronic and Science Technology of China（电子科技大学）；Ocean University of China（海洋大学）；Beijing University of Technology（北京理工大学）
EditScore: Unlocking Online RL for Image Editing via High-Fidelity Reward Modeling
University of Science and Technology of China（中国科学技术大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Zhejiang University（浙江大学）
Towards Small Object Editing: A Benchmark Dataset and A Training-Free Approach
Zhejiang University of Technology（浙江工业大学）；University of Sydney（悉尼大学）；The University of Hong Kong（香港大学）；Jilin University（吉林大学）；National University of Singapore（新加坡国立大学）
An Extensive Benchmark for Single-round and Multi-round Instruction-based Image Editing
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（厦门大学多媒体可信感知与高效计算教育部重点实验室）；National University of Singapore（新加坡国立大学）
