Although deep learning has yielded impressive performance for face recognition, many studies have shown that different networks learn different feature maps: while some networks are more receptive to pose and illumination others appear to capture more local information. Thus, in this work, we propose a deep heterogeneous feature fusion network to exploit the complementary information present in features generated by different deep convolutional neural networks (DCNNs) for template-based face recognition, where a template refers to a set of still face images or video frames from different sources which introduces more blur, pose, illumination and other variations than traditional face datasets. The proposed approach efficiently fuses the discriminative information of different deep features by 1) jointly learning the non-linear high-dimensional projection of the deep features and 2) generating a more discriminative template representation which preserves the inherent geometry of the deep features in the feature space. Experimental results on the IARPA Janus Challenge Set 3 (Janus CS3) dataset demonstrate that the proposed method can effectively improve the recognition performance. In addition, we also present a series of covariate experiments on the face verification task for in-depth qualitative evaluations for the proposed approach.
Computationally efficient, accurate, and privacy-preserving data storage and retrieval are among the key challenges faced by practical deployments of biometric identification systems worldwide. In this work, a method of protected indexing of biometric data is presented. By utilising feature-level fusion of intelligently paired templates, a multi-stage search structure is created. During retrieval, the list of potential candidate identities is successively pre-filtered, thereby reducing the number of template comparisons necessary for a biometric identification transaction. Protection of the bi…
Systems & Technology Research (STR)（系统与技术研究公司（STR））；Visionary Systems and Research (VSR)（远景系统与研究公司（VSR））；University of Oxford（牛津大学）
Face recognition performance evaluation has traditionally focused on one-to-one verification, popularized by the Labeled Faces in the Wild dataset for imagery and the YouTubeFaces dataset for videos. In contrast, the newly released IJB-A face recognition dataset unifies evaluation of one-to-many face identification with one-to-one face verification over templates, or sets of imagery and videos for a subject. In this paper, we study the problem of template adaptation, a form of transfer learning to the set of media in a template. Extensive performance evaluations on IJB-A show a surprising resu…
Panasonic R&D Center Singapore（松下新加坡研发中心）；National University of Singapore（新加坡国立大学）
Unconstrained face recognition performance evaluations have traditionally focused on Labeled Faces in the Wild (LFW) dataset for imagery and the YouTubeFaces (YTF) dataset for videos in the last couple of years. Spectacular progress in this field has resulted in saturation on verification and identification accuracies for those benchmark datasets. In this paper, we propose a unified learning framework named Transferred Deep Feature Fusion (TDFF) targeting at the new IARPA Janus Benchmark A (IJB-A) face recognition dataset released by NIST face challenge. The IJB-A dataset includes real-world u…
A robust algorithm solution is proposed for tracking an object in complex video scenes. In this solution, the bootstrap particle filter (PF) is initialized by an object detector, which models the time-evolving background of the video signal by an adaptive Gaussian mixture. The motion of the object is expressed by a Markov model, which defines the state transition prior. The color and texture features are used to represent the object, and a marginal likelihood based feature fusion approach is proposed. A corresponding object template model updating procedure is developed to account for possible…
Michigan State University（密歇根州立大学）；University at Buffalo（布法罗大学）
This paper proposes a non-interactive end-to-end solution for secure fusion and matching of biometric templates using fully homomorphic encryption (FHE). Given a pair of encrypted feature vectors, we perform the following ciphertext operations, i) feature concatenation, ii) fusion and dimensionality reduction through a learned linear projection, iii) scale normalization to unit $\ell_2$-norm, and iv) match score computation. Our method, dubbed HEFT (Homomorphically Encrypted Fusion of biometric Templates), is custom-designed to overcome the unique constraint imposed by FHE, namely the lack of …
Fusion in Your Way: Aligning Image Fusion with Heterogeneous Demands via Direct Preference Optimization
School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；Key Laboratory of Social Computing and Cognitive Intelligence (Dalian University of Technology), Ministry of Education（社会计算与认知智能重点实验室（大连理工大学），教育部）；Institute of Image Processing and Understanding, North Minzu University（北华大学图像处理与理解研究所）
College of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）；Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, Nanjing University of Science and Technology（南京理工大学智能感知与高维信息处理教育部重点实验室）
According to observations, different visual objects have different salient features in different scenarios. Even for the same object, its salient shape and appearance features may change greatly from time to time in a long-term tracking task. Motivated by them, we proposed an end-to-end feature fusion framework based on Siamese network, named FF-Siam, which can effectively fuse different features for adaptive visual tracking. The framework consists of four layers. A feature extraction layer is designed to extract the different features of the target region and search region. The extracted feat…
An Integrated Framework for the Heterogeneous Spatio-Spectral-Temporal Fusion of Remote Sensing Images
School of Resource and Environmental Science, Wuhan University（武汉大学资源与环境科学学院）；School of Geodesy and Geomatics, Wuhan University（武汉大学测绘学院）；Collaborative Innovation Center of Geospatial Technology, Wuhan University（武汉大学地球空间信息技术协同创新中心）；State Key Laboratory of Information Engineering, Survey Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）
ONERA, The French Aerospace Lab（法国国家航空航天研究中心）；Univ. Bretagne-Sud（南布列塔尼大学）；IRISA（法国国家信息与自动化研究所雷恩-布列塔尼大西洋分校）
In this work, we present a novel module to perform fusion of heterogeneous data using fully convolutional networks for semantic labeling. We introduce residual correction as a way to learn how to fuse predictions coming out of a dual stream architecture. Especially, we perform fusion of DSM and IRRG optical data on the ISPRS Vaihingen dataset over a urban area and obtain new state-of-the-art results.
Hong Kong Baptist University（香港浸会大学）；Institute of High Performance Computing, A*STAR（新加坡科技研究局高性能计算研究所）
Image-based geometric modeling and novel view synthesis based on sparse, large-baseline samplings are challenging but important tasks for emerging multimedia applications such as virtual reality and immersive telepresence. Existing methods fail to produce satisfactory results due to the limitation on inferring reliable depth information over such challenging reference conditions. With the popularization of commercial light field (LF) cameras, capturing LF images (LFIs) is as convenient as taking regular photos, and geometry information can be reliably inferred. This inspires us to use a sparse…
Change detection in heterogeneous remote sensing images is crucial for disaster damage assessment. Recent methods use homogenous transformation, which transforms the heterogeneous optical and SAR remote sensing images into the same feature space, to achieve change detection. Such transformations mainly operate on the low-level feature space and may corrupt the semantic content, deteriorating the performance of change detection. To solve this problem, this paper presents a new homogeneous transformation model termed deep homogeneous feature fusion (DHFF) based on image style transfer (IST). Unl…
The use of hand gestures can be a useful tool for many applications in the human-computer interaction community. In a broad range of areas hand gesture techniques can be applied specifically in sign language recognition, robotic surgery, etc. In the process of hand gesture recognition, proper detection, and tracking of the moving hand become challenging due to the varied shape and size of the hand. Here the objective is to track the movement of the hand irrespective of the shape, size, and color of the hand. And, for this, a motion template guided by optical flow (OFMT) is proposed. OFMT is a …
Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention
Indian Institute of Technology Ropar（印度理工学院罗帕尔分校）；RoentGen Health（伦琴健康公司）；Deakin University（迪肯大学）；University of Central Florida（中佛罗里达大学）；Queensland University of Technology（昆士兰科技大学）；Murdoch University（莫道克大学）
Dalian University of Technology（大连理工大学）；Ningbo Institute, Dalian University of Technology（大连理工大学宁波研究院）；Tiwaki Co., Ltd.（Tiwaki株式会社）
Correlation has a critical role in the tracking field, especially in recent popular Siamese-based trackers. The correlation operation is a simple fusion method that considers the similarity between the template and the search region. However, the correlation operation is a local linear matching process, losing semantic information and easily falling into a local optimum, which may be the bottleneck in designing high-accuracy tracking algorithms. In this work, to determine whether a better feature fusion method exists than correlation, a novel attention-based feature fusion network, inspired by…
With the prevalence of LiDAR sensors in autonomous driving, 3D object tracking has received increasing attention. In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in consecutive frames given an object template. Motivated by the success of transformers, we propose Point Tracking TRansformer (PTTR), which efficiently predicts high-quality 3D tracking results in a coarse-to-fine manner with the help of transformer operations. PTTR consists of three novel designs. 1) Instead of random sampling, we design Relation-Aware Sampling to preserve rel…
HetSSNet: Spatial-Spectral Heterogeneous Graph Learning Network for Panchromatic and Multispectral Images Fusion
the School of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；the School of Software Technology, Zhejiang University（浙江大学软件学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；the Innovation Center of Yangtze River Delta, Zhejiang University（浙江大学长三角创新中心）
TopoMamba: Topology-Aware Scanning and Fusion for Segmenting Heterogeneous Medical Visual Media
Dalian University of Technology（大连理工大学）；Guangdong University of Technology（广东工业大学）；Huizhou University（惠州市学院）；University of Macau（澳门大学）；Nanjing University of Posts and Telecommunications（南京邮电大学）；Hong Kong Polytechnic University（香港理工大学）
Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院（KIT））；FZI Research Center for Information Technology（FZI信息技术研究中心）
We present a new method to combine evidential top-view grid maps estimated based on heterogeneous sensor sources. Dempster's combination rule that is usually applied in this context provides undesired results with highly conflicting inputs. Therefore, we use more advanced evidential reasoning techniques and improve the conflict resolution by modeling the reliability of the evidence sources. We propose a data-driven reliability estimation to optimize the fusion quality using the Kitti-360 dataset. We apply the proposed method to the fusion of LiDAR and stereo camera data and evaluate the result…
VinAI Research（VinAI研究院）；Hanoi University of Science and Technology（河内科技大学）；University of Texas, Austin（德克萨斯大学奥斯汀分校）；Monash University（莫纳什大学）
Layer-wise model fusion via optimal transport, named OTFusion, applies soft neuron association for unifying different pre-trained networks to save computational resources. While enjoying its success, OTFusion requires the input networks to have the same number of layers. To address this issue, we propose a novel model fusion framework, named CLAFusion, to fuse neural networks with a different number of layers, which we refer to as heterogeneous neural networks, via cross-layer alignment. The cross-layer alignment problem, which is an unbalanced assignment problem, can be solved efficiently usi…
CFFormer: Cross CNN-Transformer Channel Attention and Spatial Feature Fusion for Improved Segmentation of Heterogeneous Medical Images
School of Computer Science, University of Nottingham Ningbo China（Nottingham 中国 Nottingham 理工学院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China（深圳先进技术研究所，中国科学院）；School of Mathematical and Computational Sciences, Massey University（数学与计算科学学院，马斯sey 大学）；University of Nottingham, United Kingdom（Nottingham 大学，英国）
Heterogeneous SAR-optical fusion for near-real-time land use and land cover mapping under cloud contamination: A novel framework and global benchmark dataset
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing（信息工程测绘与遥感国家重点实验室）；School of Computer Science and Information Engineering（计算机科学与信息工程学院）；Hangzhou International Innovation Institute（杭州国际创新研究院）；Oriental Space Port Research Institute（东方航天港研究院）；Hubei Luojia Laboratory（湖北珞珈实验室）
HAPNet: Toward Superior RGB-Thermal Scene Parsing via Hybrid, Asymmetric, and Progressive Heterogeneous Feature Fusion
College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）；Department of Computer Science and Engineering, Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）；MSU-BIT-SMBU Joint Research Center of Applied Mathematics, Shenzhen MSU-BIT University（深圳MSU-BIT大学应用数学联合研究中心）；Qingdao Innovation and Development Base, Harbin Institute of Technology (Weihai)（哈尔滨工业大学（威海）青岛创新与发展基地）；Department of Control Science and Engineering, Harbin Institute of Technology（哈尔滨工业大学控制科学与工程系）；Harbin Institute of Technology at Weihai（哈尔滨工业大学（威海））；Suzhou Research Institute, Harbin Institute of Technology（哈尔滨工业大学苏州研究所）；Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University（莫斯科罗蒙诺夫莫斯科国立大学计算数学与自动化系）
Dalian University of Technology（大连理工大学）；Peng Cheng Laboratory（鹏城实验室）；Remark AI
Correlation acts as a critical role in the tracking field, especially in recent popular Siamese-based trackers. The correlation operation is a simple fusion manner to consider the similarity between the template and the search region. However, the correlation operation itself is a local linear matching process, leading to lose semantic information and fall into local optimum easily, which may be the bottleneck of designing high-accuracy tracking algorithms. Is there any better feature fusion method than correlation? To address this issue, inspired by Transformer, this work presents a novel att…
GAN-HA: A generative adversarial network with a novel heterogeneous dual-discriminator network and a new attention-based fusion strategy for infrared and visible image fusion
College of New Materials and New Energies, Shenzhen Technology University（深圳技术大学新材料与新能源学院）
Liquid Fusion of Heterogeneous Representations Towards General Salient Object Detection
School of Computer Science and Artificial Intelligence, Changzhou University（常州大学计算机科学与人工智能学院）；College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院）；Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）
DRAFE: Domain-Robust Asymmetric Fusion of Heterogeneous Detection Transformers for Cross-City Fine-Grained Traffic Object Detection
Iowa State University（爱荷华州立大学）；University of Arkansas（阿肯色大学）；Parsons Corporation（帕森斯公司）；Jacobs（雅各布斯公司）；University of South Florida（南佛罗里达大学）；Neel-Schaffer, Inc.（尼尔-沙弗公司）
针对跨城市细粒度交通目标检测中细粒度识别与域泛化难兼顾的问题，提出 DRAFE 模型，通过两阶段训练及多模块融合策略，在 AI City Challenge 2026 Track 6 任务中取得 0.4022 mAP 排名第六的成绩，性能优于初步集成模型。
Single-Stream Multi-Feature Fusion with Temporal Robustness for Gait Emotion Recognition
College of Computer and Information Science, Southwest University（西南大学计算机与信息科学学院）；College of Physical Science and Technology, Southwest University（西南大学物理科学与技术学院）；Wisesoft Inc.（四川川大智胜软件股份有限公司）
In this paper, we propose a secure multibiometric system that uses deep neural networks and error-correction coding. We present a feature-level fusion framework to generate a secure multibiometric template from each user's multiple biometrics. Two fusion architectures, fully connected architecture and bilinear architecture, are implemented to develop a robust multibiometric shared representation. The shared representation is used to generate a cancelable biometric template that involves the selection of a different set of reliable and discriminative features for each user. This cancelable temp…
Duke University（杜克大学）；Geometric Data Analytics, Inc.（几何数据分析公司）
In this work, we address fusion of heterogeneous sensor data using wavelet-based summaries of fused self-similarity information from each sensor. The technique we develop is quite general, does not require domain specific knowledge or physical models, and requires no training. Nonetheless, it can perform surprisingly well at the general task of differentiating classes of time-ordered behavior sequences which are sensed by more than one modality. As a demonstration of our capabilities in the audio to video context, we focus on the differentiation of speech sequences. Data from two or more modal…
