Existing Infrared and Visible Image Fusion (IVIF) methods typically assume high-quality inputs. However, when handing degraded images, these methods heavily rely on manually switching between different pre-processing techniques. This decoupling of degradation handling and image fusion leads to significant performance degradation. In this paper, we propose a novel VLM-Guided Degradation-Coupled Fusion network (VGDCFusion), which tightly couples degradation modeling with the fusion process and leverages vision-language models (VLMs) for degradation-aware perception and guided suppression. Specifically, the proposed Specific-Prompt Degradation-Coupled Extractor (SPDCE) enables modality-specific degradation awareness and establishes a joint modeling of degradation suppression and intra-modal feature extraction. In parallel, the Joint-Prompt Degradation-Coupled Fusion (JPDCF) facilitates cross-modal degradation perception and couples residual degradation filtering with complementary cross-modal feature fusion. Extensive experiments demonstrate that our VGDCFusion significantly outperforms existing state-of-the-art fusion approaches under various degraded image scenarios. Our code is available at https://github.com/Lmmh058/VGDCFusion.
Unified Restoration-Perception Learning: Maritime Infrared-Visible Image Fusion and Segmentation
Laboratory of Underwater Acoustic Communication and Marine Information Technology, Ministry of Education, Xiamen University（厦门大学水声通信与海洋信息技术教育部重点实验室）；PAMI Research Group, Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系PAMI研究组）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区网络空间安全学院）
CAWM-Mamba: A unified model for infrared-visible image fusion and compound adverse weather restoration
School of Physics（物理学院）；Optoelectronic Engineering, Foshan University, Foshan 528225, China（光学电子工程学院，佛山大学，佛山528225，中国）；Guangdong-HongKong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology, Foshan 528225, China（粤港澳联合智能微纳光电子技术实验室，佛山528225，中国）；School of Mechanical Electronic（机械电子学院）；Information Engineering, China University of Mining and Technology, Beijing 100083, China（信息工程学院，中国矿业大学，北京100083，中国）
Liaoning Normal University（辽宁师范大学）；Dalian University of Technology（大连理工大学）
General deep learning-based methods for infrared and visible image fusion rely on the unsupervised mechanism for vital information retention by utilizing elaborately designed loss functions. However, the unsupervised mechanism depends on a well designed loss function, which cannot guarantee that all vital information of source images is sufficiently extracted. In this work, we propose a novel interactive feature embedding in self-supervised learning framework for infrared and visible image fusion, attempting to overcome the issue of vital information degradation. With the help of self-supervis…
Infrared Image Deturbulence Restoration Using Degradation Parameter-Assisted Wide & Deep Learning
Beihang University（北京航空航天大学）；Image Processing Center（图像处理中心）；The State Key Laboratory of Virtual Reality Technology and Systems（虚拟现实技术与系统国家重点实验室）；Beijing Advanced Innovation Center for Biomedical Engineering（北京生物医学工程高精尖创新中心）
Understanding Degradation with Vision Language Model
School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University（人工智能学院、光学与电子学（iOPEN）、西北工业大学）；Honors College, Northwestern Polytechnical University（荣誉学院、西北工业大学）；Shanghai AI Laboratory（上海人工智能实验室）；School of Artificial Intelligence, Shanghai Jiaotong University（人工智能学院、上海交通大学）
MonoIR-RS: Infrared Remote Sensing Vision-Language Learning with CLIP and VLM Adaptation
China University of Petroleum-Beijing at Karamay（中国石油大学（北京）克拉玛依校区）；Guizhou University（贵州大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Shanghai Jiao Tong University（上海交通大学）
JarvisIR: Elevating Autonomous Driving Perception with Intelligent Image Restoration
Xiamen University（厦门大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Bytedance’s Pico（字节跳动Pico）；Tencent（腾讯）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；The Chinese University of Hong Kong（香港中文大学）
JaiLIP: Jailbreaking Vision-Language Models via Loss Guided Image Perturbation
Knight Foundation School of Computing and Information Sciences (KFSCIS)（骑士基金会计算与信息科学学院）；Florida International University（佛罗里达国际大学）；Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab)（可持续性、优化与互依赖网络学习实验室）
Small Vision-Language Models Know When They Are Wrong But Cannot Say So: A Two-Model Study of Stated versus Internal Confidence Under Realistic Image Degradation
SaFeR-VLM: Toward Safety-aware Fine-grained Reasoning in Multimodal Models
West China Biomedical Big Data Center, West China Hospital, SCU（西昌生物医学大数据中心、西昌医院、SCU）；NTU；USTC；TeleAI, China Telecom（TeleAI、中国电信）；BUPT；Tsinghua University（清华大学）；HKUST(Guangzhou)（HKUST（广州））
Lost in Visual Translation: A VLM-Assisted Perceptual-Semantic Coherence Framework for EEG-to-Image Reconstruction
Mahindra University（马欣德拉大学）；MU-VT Interdisciplinary Advanced Research Centre for Transformative Technologies, Mahindra University（马欣德拉大学MU-VT变革性技术跨学科高级研究中心）
SeqVLM: Proposal-Guided Multi-View Sequences Reasoning via VLM for Zero-Shot 3D Visual Grounding
School of Informatics, Xiamen University（厦门大学信息学院）；School of Computer Science, Nanjing University（南京大学计算机科学学院）；School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（教育部多媒体可信感知与高效计算重点实验室，厦门大学）
DPOison-VL: Clean Preference Poisoning in Vision-Language Direct Preference Optimization
School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；Shenzhen Loop Area Institute（深圳河套学院）；Shanghai Research Institute for Intelligent Autonomous Systems（上海智能自主系统研究院）
