Multimodal image matching seeks pixel-level correspondences between images of different modalities, crucial for cross-modal perception, fusion and analysis. However, the significant appearance differences between modalities make this task challenging. Due to the scarcity of high-quality annotated datasets, existing deep learning methods that extract modality-common features for matching perform poorly and lack adaptability to diverse scenarios. Vision Foundation Model (VFM), trained on large-scale data, yields generalizable and robust feature representations adapted to data and tasks of various modalities, including multimodal matching. Thus, we propose DistillMatch, a multimodal image matching method using knowledge distillation from VFM. DistillMatch employs knowledge distillation to build a lightweight student model that extracts high-level semantic features from VFM (including DINOv2 and DINOv3) to assist matching across modalities. To retain modality-specific information, it extracts and injects modality category information into the other modality's features, which enhances the model's understanding of cross-modal correlations. Furthermore, we design V2I-GAN to boost the model's generalization by translating visible to pseudo-infrared images for data augmentation. Experiments show that DistillMatch outperforms existing algorithms on public datasets.
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；Key Laboratory of Intelligent Bionic Unmanned Systems, Ministry of Education（教育部智能仿生无人系统重点实验室）；School of Artificial Intelligence, University of Science and Technology Beijing（北京科技大学人工智能学院）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）
Correlation-Decoupled Knowledge Distillation for Multimodal Sentiment Analysis with Incomplete Modalities
Academy for Engineering and Technology, Fudan University（复旦大学工程技术学院）；Jilin Provincial Key Laboratory of Intelligence Science and Engineering（吉林省智能科学与工程重点实验室）；Engineering Research Center of AI and Robotics, Ministry of Education（教育部人工智能与机器人工程研究中心）
Nanjing University of Science and Technology（南京理工大学）
Human multimodal emotion recognition (MER) aims to perceive human emotions via language, visual and acoustic modalities. Despite the impressive performance of previous MER approaches, the inherent multimodal heterogeneities still haunt and the contribution of different modalities varies significantly. In this work, we mitigate this issue by proposing a decoupled multimodal distillation (DMD) approach that facilitates flexible and adaptive crossmodal knowledge distillation, aiming to enhance the discriminative features of each modality. Specially, the representation of each modality is decouple…
Decoupled Hierarchical Distillation for Multimodal Emotion Recognition
School of Computer Science and Engineering, and the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Southeast University（计算机科学与工程学院，新一代人工智能技术及跨学科应用重点实验室，东南大学）；School of Computer Science and Engineering, Nanjing University of Science and Technology（计算机科学与工程学院，南京理工大学）；Institute of Intelligent Information Processing, Taizhou University（智能信息处理研究院，台州大学）；School of Engineering Science, University of Chinese Academy of Sciences（工程科学学院，中国科学院大学）；Peng Cheng Laboratory（鹏城实验室）；School of Computer Science and Engineering, Nanyang Technological University（计算机科学与工程学院，南洋理工大学）
University of Southern California（南加州大学）；Facebook AI
To reduce a model size but retain performance, we often rely on knowledge distillation (KD) which transfers knowledge from a large "teacher" model to a smaller "student" model. However, KD on multimodal datasets such as vision-language tasks is relatively unexplored, and digesting multimodal information is challenging since different modalities present different types of information. In this paper, we perform a large-scale empirical study to investigate the importance and effects of each modality in knowledge distillation. Furthermore, we introduce a multimodal knowledge distillation framework…
We propose a technique that tackles action detection in multimodal videos under a realistic and challenging condition in which only limited training data and partially observed modalities are available. Common methods in transfer learning do not take advantage of the extra modalities potentially available in the source domain. On the other hand, previous work on multimodal learning only focuses on a single domain or task and does not handle the modality discrepancy between training and testing. In this work, we propose a method termed graph distillation that incorporates rich privileged inform…
Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning
The City College of New York（纽约城市学院）；INSAIT；Sofia University “St. Kliment Ohridski”（圣克莱门特奥赫里德斯基索非亚大学）；Julius-Maximilians-Universität Würzburg（维尔茨堡大学）；State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所人工智能安全国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Shenzhen University of Advanced Technology（深圳先进技术研究院）
Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation
NVIDIA（英伟达公司）；Fudan University（复旦大学）；East China Normal University（华东师范大学）；Beijing Institute of Technology（北京理工大学）；Nanjing University（南京大学）；Nankai University（南开大学）
Purify-then-Align: Towards Robust Human Sensing under Modality Missing with Knowledge Distillation from Noisy Multimodal Teacher
School of Software Engineering, Xi’an Jiaotong University（西安交通大学软件工程学院）；Institute of Computer Science, Universität Bern（伯尔尼大学计算机科学研究所）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
Visual-OPSD: Cross-Modal On-Policy Self-Distillation for Efficient Unified Multimodal Reasoning
Xi’an Jiaotong University（西安交通大学）；MOE KLINNS Lab（MOE KLINNS实验室）；Shaanxi Province Key Laboratory of Big Data Knowledge Engineering（陕西省大数据知识工程重点实验室）；Sun Yat-sen University（中山大学）
Hong Kong University of Science and Technology（香港科技大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）
The recent large-scale vision-language pre-training (VLP) of dual-stream architectures (e.g., CLIP) with a tremendous amount of image-text pair data, has shown its superiority on various multimodal alignment tasks. Despite its success, the resulting models are not capable of multimodal generative tasks due to the weak text encoder. To tackle this problem, we propose to augment the dual-stream VLP model with a textual pre-trained language model (PLM) via vision-language knowledge distillation (VLKD), enabling the capability for multimodal generation. VLKD is pretty data- and computation-efficie…
Enriched text-guided variational multimodal knowledge distillation network (VMD) for automated diagnosis of plaque vulnerability in 3D carotid artery MRI
department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University（放射医学与核医学科，宣武医院，首都医科大学）；Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics（北京磁共振成像与脑信息学重点实验室）；Beijing United Imaging Research Institute of Intelligent Imaging（北京智能成像联合研究院）
Monash University（莫纳什大学）；Inception Institute of Artificial Intelligence（Inception人工智能研究院）
Recently, a lot of automated white blood cells (WBC) or leukocyte classification techniques have been developed. However, all of these methods only utilize a single modality microscopic image i.e. either blood smear or fluorescence based, thus missing the potential of a better learning from multimodal images. In this work, we develop an efficient multimodal architecture based on a first of its kind multimodal WBC dataset for the task of WBC classification. Specifically, our proposed idea is developed in two steps - 1) First, we learn modality specific independent subnetworks inside a single ne…
We deal with the problem of information fusion driven satellite image/scene classification and propose a generic hallucination architecture considering that all the available sensor information are present during training while some of the image modalities may be absent while testing. It is well-known that different sensors are capable of capturing complementary information for a given geographical area and a classification module incorporating information from all the sources are expected to produce an improved performance as compared to considering only a subset of the modalities. However, t…
OmniOPSD: Rationale-Privileged On-Policy Self-Distillation for Affective Computing
Shenzhen University（深圳大学）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））；Tsinghua University（清华大学）；Shanghai Jiao Tong University（上海交通大学）；University of Science and Technology of China（中国科学技术大学）；Shenzhen Technology University（深圳技术大学）；Tongji University（同济大学）；Huawei（华为）
Beijing University of Posts and Telecommunications（北京邮电大学）；ByteDance（字节跳动）；USTC（中国科学技术大学）；Beijing Key Laboratory of Network System and Network Culture（北京网络系统与网络文化重点实验室）；Key Laboratory of Interactive Technology and Experience System, Ministry of Culture and Tourism（文化和旅游部互动技术与体验系统重点实验室）；Zhongguancun Academy（中关村科学城）
MuDD: A Multimodal Deception Detection Dataset and GSR-Guided Progressive Distillation for Non-Contact Deception Detection
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；School of Information and Software Engineering, University of Electronic Science and Technology of China（电子科技大学信息与软件工程学院）
University of Liège（列日大学）；Aalborg University（奥尔堡大学）
Monitoring the occupancy of public sports facilities is essential to assess their use and to motivate their construction in new places. In the case of a football field, the area to cover is large, thus several regular cameras should be used, which makes the setup expensive and complex. As an alternative, we developed a system that detects players from a unique cheap and wide-angle fisheye camera assisted by a single narrow-angle thermal camera. In this work, we train a network in a knowledge distillation approach in which the student and the teacher have different modalities and a different vi…
BAS-OPD: Budget-Aware Selective On-Policy Self-Distillation for Fine-Grained Multimodal Perception
HFIPS, Chinese Academy of Sciences（中国科学院合肥物质科学研究院）；University of Science and Technology of China（中国科学技术大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge
Fast School of Computing, National University of Computer and Emerging Sciences（国家计算机与新兴科学大学快速计算学院）；Universiti Malaya（马来亚大学）；School of Professional Education and Executive Development, The Hong Kong Polytechnic University（香港理工大学专业教育与执行发展学院）；COMSATS University Islamabad, Wah Campus（伊斯兰堡COMSATS大学瓦校区）；Intelligent Transportation Systems University of Tennessee-Oak Ridge Innovation Institute’s Energy Storage and Transportation Convergent Research Initiative（智能交通系统田纳西大学奥克岭创新研究所能源存储与交通融合研究计划）；Center for research in Computer Vision, University of Central Florida（计算机视觉研究中心，佛罗里达中央大学）
