Generative AI (GenAI) image editors, such as Nano Banana, produce visually compelling results for retouching tasks, enabling non-experts to edit images through text prompts alone. However, the generative nature of these models often introduces spatial misalignment, texture distortion, and content hallucination, all of which are detrimental to downstream workflows that require pixel-level fidelity. We identify a problem setting we call "structure-preserving GenAI fusion" for black-box GenAI image retouching: retain the perceptual enhancements of a GenAI output while enforcing structural faithfulness to the original input image. To address this problem, we propose a post-processing framework that fuses an input image with its GenAI-enhanced counterpart by first establishing coarse spatial and photometric correspondences, then performing a fusion stage that transfers desired enhancements while suppressing hallucinated content. In the absence of direct prior work in this setting, we evaluate our framework against representative methods from photorealistic style transfer and image fusion. Our experiments demonstrate that our method better preserves aesthetic quality while maintaining pixel-level structural consistency and the input resolution.
HIME: Mitigating Object Hallucinations in LVLMs via Hallucination Insensitivity Model Editing
School of Information and Communication Technology, Griffith University, Australia（信息与通信技术学院，格里菲斯大学）；Data61, CSIRO, Australia（Data61，澳大利亚联邦科学与工业研究组织）；School of Engineering, Macquarie University, Sydney, Australia（工程学院，麦觉大学）；School of Information Systems, Queensland University of Technology, Australia（信息系统学院，昆士兰技术大学）
Beyond Global Editing: Per-Instance Disentangled Subspaces for Training-Free Hallucination Mitigation in LVLMs
Macquarie University（麦考瑞大学）；York University（约克大学）；Northern Illinois University（北伊利诺伊大学）；University of Technology Sydney（悉尼科技大学）；North South University（北南大学）；Lancaster University（兰卡斯特大学）；Australian National University（澳大利亚国立大学）
Beyond Textual CoT: Interleaved Text-Image Chains with Deep Confidence Reasoning for Image Editing
Shanghai Jiaotong University（上海交通大学）；Huawei Noah’s Ark Lab（华为诺亚实验室）；East China Normal University（华东师范大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）
Generative AI for Film Creation: A Survey of Recent Advances
Google（谷歌）；University of California, Santa Barbara（加利福尼亚大学圣巴巴拉分校）；MYStudio；Harvard University（哈佛大学）；Reality Hack；SUNY Buffalo（纽约州立大学布法罗分校）；Onceness；University of Southampton（南安普顿大学）；New York University（纽约大学）；Communication University of China（中国传媒大学）；University of Southern California（南加利福尼亚大学）；Dodge College of Film and Media Arts（道奇电影与媒体艺术学院）；Pratt Institute（普拉特学院）；Rubyspot；MIT（麻省理工学院）；Netflix（网飞）；Hong Kong University of Science and Technology（香港科技大学）
HulluEdit: Single-Pass Evidence-Consistent Subspace Editing for Mitigating Hallucinations in Large Vision-Language Models
Beijing University of Posts and Telecommunications（北京邮电大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing Jiaotong University（北京交通大学）
