In this paper, we introduce ResNetVLLM (ResNet Vision LLM), a novel cross-modal framework for zero-shot video understanding that integrates a ResNet-based visual encoder with a Large Language Model (LLM. ResNetVLLM addresses the challenges associated with zero-shot video models by avoiding reliance on pre-trained video understanding models and instead employing a non-pretrained ResNet to extract visual features. This design ensures the model learns visual and semantic representations within a unified architecture, enhancing its ability to generate accurate and contextually relevant textual descriptions from video inputs. Our experimental results demonstrate that ResNetVLLM achieves state-of-the-art performance in zero-shot video understanding (ZSVU) on several benchmarks, including MSRVTT-QA, MSVD-QA, TGIF-QA FrameQA, and ActivityNet-QA.
3D Modality-Aware Pre-training for Vision-Language Model in MRI Multi-organ Abnormality Detection
School of Electronic, Electrical Engineering and Physics, Fujian University of Technology（福建工程学院电子电气工程学院）；School of Computer Science and Engineering, Southeast University, China（东南大学计算机科学与工程学院）；Department of Medical Imaging, Suzhou Traditional Chinese Medicine Hospital, China（苏州中医医院影像科）
Breaking Modality Heterogeneity in Low-Bit Quantization for Large Vision-Language Models
VCIP, College of Computer Science, Nankai University（南开大学计算机科学学院VCIP）；D-ITET, ETH Zürich（苏黎世联邦理工学院D-ITET）；OPPO Research Institute（OPPO研究院）；Department of Computing, Hong Kong Polytechnic University（香港理工大学计算机系）
Unveiling the Fragility of Vision-Language Models: Multi-Modal Adversarial Synergy via Texture-Constrained Perturbations and Cross-Modal Optimization
School of Software Engineering, Huazhong University of Science and Technology（华中科技大学软件学院）；Nanyang Technological University, Singapore（新加坡南洋理工大学）；University College London（伦敦大学学院）
CoMT: A Novel Benchmark for Chain of Multi-modal Thought on Large Vision-Language Models
School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities, Soochow University（苏州大学省级高校数据智能与先进计算重点实验室）；Research Center for SCIR, Harbin Institute of Technology（哈尔滨工业大学SCIR研究中心）；National University of Singapore（新加坡国立大学）
FusionRS: A Large-Scale RGB-Infrared-Style Remote Sensing Dataset for Cross-Modal Vision-Language Learning
China University of Petroleum-Beijing at Karamay（中国石油大学（北京）克拉玛依校区）；University of Electronic Science and Technology of China（电子科技大学）；Tianjin University（天津大学）
MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning
Peking University（北京大学）；School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；BIGAI（北京智源人工智能研究院）；Paul G. Allen School of Computer Science and Engineering, University of Washington（华盛顿大学保罗·G·艾伦计算机科学与工程学院）；Beijing Jiaotong University（北京交通大学）
COSMOS: Cross-Modality Self-Distillation for Vision Language Pre-training
Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Helmholtz Munich（亥姆霍兹慕尼黑中心）；Munich Data Science Institute (MDSI)（慕尼黑数据科学研究所）
Stream-Omni: Simultaneous Multimodal Interactions with Large Language-Vision-Speech Model
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS)（中国科学院智能信息处理重点实验室）；Key Laboratory of AI Safety, Chinese Academy of Sciences（中国科学院人工智能安全重点实验室）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学）
Red-teaming the Multimodal Reasoning: Jailbreaking Vision-Language Models via Cross-modal Entanglement Attacks
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；People’s Public Security University of China（中国人民公安大学）
LongVALE: Vision-Audio-Language-Event Benchmark Towards Time-Aware Omni-Modal Perception of Long Videos
Southern University of Science and Technology（南方科技大学）；University of Birmingham（伯明翰大学）；University of Electronic Science and Technology of China（电子科技大学）；The University of Hong Kong（香港大学）；University of Manchester（曼彻斯特大学）
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Electrical and Information Engineering, The University of Sydney（悉尼大学电气与信息工程学院）
MedUnifier: Unifying Vision-and-Language Pre-training on Medical Data with Vision Generation Task using Discrete Visual Representations
MedVisAI Lab（MedVisAI实验室）；ECE, Northwestern University（电子工程系，西北大学）；Institute for Infocomm Research (I 2 R), A*STAR, Singapore（信息与通信研究所（I2R），A*STAR，新加坡）；Lee Kong Chian School of Medicine, Nanyang Technological University（Lee Kong Chian医学院，南洋理工大学）
Enabling Vision and Cross-Modal Learning for Multimodal Stroke Recurrence Prediction: An Interpretable Two-Step Framework
UMIT TIROL – Private University for Health Sciences and Health Technology（UMIT TIROL – 健康科学与健康技术私立大学）；VASCage – Centre on Clinical Stroke Research（VASCage – 临床卒中研究中心）；Medical University of Innsbruck（因斯布鲁克医科大学）
Twins-PainViT: Towards a Modality-Agnostic Vision Transformer Framework for Multimodal Automatic Pain Assessment using Facial Videos and fNIRS
Computational Biomedicine Laboratory of the Foundation for Research and Technology (FORTH)（希腊国家研究与技术基金会计算生物医学实验室）；Department of Electrical & Computer Engineering Hellenic Mediterranean University（希腊地中海大学电气与计算机工程系）
Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学智能产业研究院（AIR））
