Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in general visual understanding. However, their application to safety-critical driving scenarios remains limited by an inability to accurately perceive and reason about rare high-stakes dynamic events, such as collisions or near-collisions. To address this, we introduce a pipeline that enhances MLLM perception by fusing downsampled video frames with synchronized high-frequency telematics data (IMU and GPS) and semantic insights from specialized computer vision models. Our pipeline generates high-quality pseudo-labels, including descriptive captions and question-answer pairs, specifically designed to train MLLMs to identify and describe Safety-Critical Events (SCEs) in real-world driving footage. We show the effectiveness of our approach fine-tuning the open-source QwenVL-2.5 model via DoRA adapters: our experiments demonstrate significant improvements in identifying and explaining safety-critical events, with fewer than 50M trainable parameters and limited computational budget.
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））
Can't See the Forest for the Trees: Benchmarking Multimodal Safety Awareness for Multimodal LLMs
Renmin University of China（中国人民大学）；Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Chinese University of Hong Kong（香港中文大学）；Johns Hopkins University（约翰霍普金斯大学）；Hong Kong University of Science and Technology（香港科技大学）；Tencent（腾讯）
SafeEraser: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Southeast University（东南大学）；Ant Group, Alibaba（蚂蚁集团）
GuardAlign: Test-time Safety Alignment in Multimodal Large Language Models
MoE Key Lab of BIPC, University of Science and Technology of China（摩埃关键实验室，中国科学技术大学）；Nanyang Technological University（南洋理工大学）；National University of Singapore（新加坡国立大学）；Tianjin University（天津大学）
VTPerception-R1: Enhancing Multimodal Reasoning via Explicit Visual and Textual Perceptual Grounding
Fudan University（复旦大学）；Shanghai AI Laboratory（上海人工智能实验室）；The University of Hong Kong（香港大学）；Shenzhen University（深圳大学）；University of Science and Technology of China（中国科学技术大学）
Carnegie Mellon University（卡内基梅隆大学）；Waseda University（早稻田大学）；Yahoo Japan Corporation（雅虎日本公司）
Question categorization and expert retrieval methods have been crucial for information organization and accessibility in community question & answering (CQA) platforms. Research in this area, however, has dealt with only the text modality. With the increasing multimodal nature of web content, we focus on extending these methods for CQA questions accompanied by images. Specifically, we leverage the success of representation learning for text and images in the visual question answering (VQA) domain, and adapt the underlying concept and architecture for automated category classification and exper…
Targeted Interpretable Safety Neuron Enhancement for Multilingual Vision-Language Large Models
Nanjing University of Science and Technology（南京理工大学）；National University of Singapore（新加坡国立大学）；Beihang University（北京航空航天大学）；Nanjing Forestry University（南京林业大学）
Beyond Visual Safety: Jailbreaking Multimodal Large Language Models for Harmful Image Generation via Semantic-Agnostic Inputs
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（网络与交换技术国家重点实验室，北京邮电大学）；School of Cyberspace Security, Beijing University of Posts and Telecommunications（网络安全学院，北京邮电大学）
UniSAFE: A Comprehensive Benchmark for Safety Evaluation of Unified Multimodal Models
KAIST AI（KAIST人工智能研究院）；Department of Computer Science and Engineering, UNIST（UNIST计算机科学与工程系）；Department of Mathematical Sciences, KAIST（KAIST数学科学系）；University of Toronto（多伦多大学）；KAIST CS（KAIST计算机科学系）
Multimodal Benchmark for Safety Assessment in Industrial Inspection Scenarios
Department of Automation, Tsinghua University（清华大学自动化系）；Institute for Embodied Intelligence and Robotics, Tsinghua University（清华大学具身智能与机器人研究所）；TetraBOT Intelligence Co., Ltd.（天博智能科技有限公司）；DAMO Academy, Alibaba Group（阿里巴巴达摩院）；School of Automation, Southeast University（东南大学自动化学院）
Enhancing multimodal cooperation via sample-level modality valuation
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Beijing Key Laboratory of Big Data Management and Analysis Methods（北京大数据管理与分析方法重点实验室）
