Swarm Robotics Lab NCRA（国家机器人与自动化中心群体机器人实验室）；Deutsches Elektronen-Synchrotron DESY（德国电子同步加速器研究所）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of Engineering and Technology Taxila（塔克西拉工程技术大学）；Information Technology University（信息技术大学）
Muhammad Saad Saeed, Shah Nawaz, Muhammad Haris Khan, Muhammad Zaigham Zaheer, Karthik Nandakumar, Muhammad Haroon Yousaf, Arif Mahmood
With the rapid growth of social media platforms, users are sharing billions of multimedia posts containing audio, images, and text. Researchers have focused on building autonomous systems capable of processing such multimedia data to solve challenging multimodal tasks including cross-modal retrieval, matching, and verification. Existing works use separate networks to extract embeddings of each modality to bridge the gap between them. The modular structure of their branched networks is fundamental in creating numerous multimodal applications and has become a defacto standard to handle multiple modalities. In contrast, we propose a novel single-branch network capable of learning discriminative representation of unimodal as well as multimodal tasks without changing the network. An important feature of our single-branch network is that it can be trained either using single or multiple modalities without sacrificing performance. We evaluated our proposed single-branch network on the challenging multimodal problem (face-voice association) for cross-modal verification and matching tasks with various loss formulations. Experimental results demonstrate the superiority of our proposed single-branch network over the existing methods in a wide range of experiments. Code: https://github.com/msaadsaeed/SBNet
Modality Invariant Multimodal Learning to Handle Missing Modalities: A Single-Branch Approach
Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of Engineering and Technology Taxila（塔克西拉工程大学）；Dalhousie University（达尔豪斯大学）；Interuniversity Microelectronics Centre (IMEC)（校际微电子中心）；Johannes Kepler University Linz（约翰开普勒林茨大学）；Linz Institute of Technology（林茨理工学院）
Chameleon: Images Are What You Need For Multimodal Learning Robust To Missing Modalities
IMT School for Advanced Studies of Lucca（卢卡高等研究院）；Institute of Computational Perception, Johannes Kepler University（约翰开普勒大学计算感知研究所）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of Engineering and Technology, Taxila（塔克西拉工程技术大学）；Dalhousie University（戴尔豪斯大学）；Interuniversity Microelectronics Centre (IMEC)（微电子学研究中心）；Linz Institute of Technology（林茨理工学院）
EGM-Det: Entropy-Guided Multimodal Adaptive Fusion for UAV RGB-IR Object Detection
School of Cybersecurity, Northwestern Polytechnical University（西北工业大学网络空间安全学院）；School of Automation and Software Engineering, Shanxi University（山西大学自动化与软件学院）
Dual-branch Prompting for Multimodal Machine Translation
School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算机与人工智能学院）；School of Computer and Software Engineering, Xihua University（西华大学计算机与软件工程学院）；School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine（成都中医药大学针灸推拿学院）
Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge
School of Computer Science and Engineering, Southeast University, Nanjing 210096, China（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China（新一代人工智能技术及其跨学科应用关键实验室）
LIMSSR: LLM-Driven Sequence-to-Score Reasoning under Training-Time Incomplete Multimodal Observations
Fujian Provincial Key Laboratory of Networking Computing（网络计算与智能信息处理福建省重点实验室）；Intelligent Information Processing, College of Computer（智能信息处理学院）；Data Science, Fuzhou University（数据科学，福州大学）；Engineering Research Center of Big Data Intelligence, Ministry of Education（大数据智能工程研究中心，教育部）；Wangxuan Institute of Computer Technology, Peking University（王宣计算机技术研究所，北京大学）
Cross-Modal Mapping and Dual-Branch Reconstruction for 2D-3D Multimodal Industrial Anomaly Detection
CNR-ISASI, Institute of Applied Sciences and Intelligent Systems / National Research Council of Italy（CNR-ISASI应用科学与智能系统研究所/意大利国家研究理事会）；CNR-STIIMA, Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing（CNR-STIIMA智能工业技术与系统先进制造研究所）；Institute d'Electronique de Microelectronique et de Nanotechnologie (IEMN) / Universite Polytechnique Hauts de France（微电子与纳米技术研究所（IEMN）/法国高等法国理工学院）
An analysis of HOI: using a training-free method with multimodal visual foundation models when only the test set is available, without the training set
Parameter-Inverted Image Pyramid Networks for Visual Perception and Multimodal Understanding
Shanghai Jiao Tong University（上海交通大学）；Tsinghua University（清华大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；Sensetime
Cross-lingual Visual Pre-training for Multimodal Machine Translation
Imperial College London（伦敦帝国理工学院）；Hacettepe University（哈斯特帕大学）；Koç University（科奇大学）；University of Sheffield（谢菲尔德大学）；ADAPT - Dublin City University（ADAPT - 都柏林城市大学）
iGVLM: Dynamic Instruction-Guided Vision Encoding for Question-Aware Multimodal Understanding
School of Computer Science（计算机科学学院）；Technology, Huazhong University of Science（科技，华中科技大学）；Li Auto Inc.（Li汽车公司）；Institute of AI for Industries, Chinese Academy of Sciences（产业人工智能研究所，中国科学院）
Speaker Recognition in Realistic Scenario Using Multimodal Data
University of Engineering and Technology Taxila（塔克西拉工程技术大学）；Swarm Robotics Lab NCRA（国家机器人与自动化研究所群体机器人实验室）；Deutsches Elektronen-Synchrotron DESY（德国电子同步加速器研究所）
A Multimodal RAG Framework for Housing Damage Assessment: Collaborative Optimization of Image Encoding and Policy Vector Retrieval
University of California, Berkeley（加州大学伯克利分校）；Blum Center for Developing Economies（布鲁姆发展中经济体中心）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；McKelvey School of Engineering, Washington University in St. Louis（圣路易斯华盛顿大学麦凯维工程学院）
