Improved dual channel pulse coupled neural network and its application to multi-focus image fusion
School of IoT Engineering, Jiangnan University（江南大学物联网工程学院）；College of Mathematics and Information Science, East China Institute of Technology（东华理工大学数学与信息科学学院）
This paper presents an improved dual channel pulse coupled neural network (IDC-PCNN) model for image fusion. The model can overcome some defects of standard PCNN model. In this fusion scheme, the multiplication rule is replaced by addition rule in the information fusion pool of dual channel PCNN (DC-PCNN) model. Meanwhile the sum of modified Laplacian (SML) measure is adopted, which is better than other focus measures. This method not only inherits the good characteristics of the standard PCNN model but also enhances the computing efficiency and fusion quality. The performance of the proposed method is evaluated by using four criteria including average cross entropy, root mean square error, peak value signal to noise ratio and structure similarity index. Comparative studies show that the proposed fusion algorithm outperforms the standard PCNN method and the DC-PCNN method.
MAUGIF: Mechanism-Aware Unsupervised General Image Fusion via Dual Cross-Image Autoencoders
School of Mathematics, Hunan University（湖南大学数学学院）；College of Information Engineering, Zhejiang University of Technology（浙江工业大学信息工程学院）；Department of Electrical and Electronic Engineering, The University of Hong Kong（香港大学电子与电气工程系）
Deep learning is a rapidly developing approach in the field of infrared and visible image fusion. In this context, the use of dense blocks in deep networks significantly improves the utilization of shallow information, and the combination of the Generative Adversarial Network (GAN) also improves the fusion performance of two source images. We propose a new method based on dense blocks and GANs , and we directly insert the input image-visible light image in each layer of the entire network. We use SSIM and gradient loss functions that are more consistent with perception instead of mean square e…
CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion
Xi’an Jiaotong University（西安交通大学）；ETH Zürich（苏黎世联邦理工学院）；Northwestern Polytechnical University（西北工业大学）；Research and Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）；Harvard University（哈佛大学）；University of Würzburg（维尔茨堡大学）
RVS College of Engineering and Technology（RVS工程技术学院）
In recent years, many research achievements are made in the medical image fusion field. Fusion is basically extraction of best of inputs and conveying it to the output. Medical Image fusion means that several of various modality image information is comprehended together to form one image to express its information. The aim of image fusion is to integrate complementary and redundant information. In this paper, a multimodal image fusion algorithm based on the dual-tree complex wavelet transform (DT-CWT) and adaptive particle swarm optimization (APSO) is proposed. Fusion is achieved through the …
DAF-Net: A Dual-Branch Feature Decomposition Fusion Network with Domain Adaptive for Infrared and Visible Image Fusion
University of Electronic Science and Technology of China（电子科技大学）；School of Information and Communication Engineering（信息与通信工程学院）；School of Resources and Environment（资源与环境学院）
Hanyang University（汉阳大学）；Korea Advanced Institute of Science & Technology (KAIST)（韩国科学技术院（KAIST））
Fusing data from cameras and LiDAR sensors is an essential technique to achieve robust 3D object detection. One key challenge in camera-LiDAR fusion involves mitigating the large domain gap between the two sensors in terms of coordinates and data distribution when fusing their features. In this paper, we propose a novel camera-LiDAR fusion architecture called, 3D Dual-Fusion, which is designed to mitigate the gap between the feature representations of camera and LiDAR data. The proposed method fuses the features of the camera-view and 3D voxel-view domain and models their interactions through …
Saliency computation has become a popular research field for many applications due to the useful information provided by saliency maps. For a saliency map, local relations around the salient regions in multi-channel perspective should be taken into consideration by aiming uniformity on the region of interest as an internal approach. And, irrelevant salient regions have to be avoided as much as possible. Most of the works achieve these criteria with external processing modules; however, these can be accomplished during the conspicuity map fusion process. Therefore, in this paper, a new model is…
Photoplethysmographic imaging is a camera-based solution for non-contact cardiovascular monitoring from a distance. This technology enables monitoring in situations where contact-based devices may be problematic or infeasible, such as ambulatory, sleep, and multi-individual monitoring. However, extracting the blood pulse waveform signal is challenging due to the unknown mixture of relevant (pulsatile) and irrelevant pixels in the scene. Here, we design and implement a signal fusion framework, FusionPPG, for extracting a blood pulse waveform signal with strong temporal fidelity from a scene wit…
The task of crowd counting in varying density scenes is an extremely difficult challenge due to large scale variations. In this paper, we propose a novel dual path multi-scale fusion network architecture with attention mechanism named SFANet that can perform accurate count estimation as well as present high-resolution density maps for highly congested crowd scenes. The proposed SFANet contains two main components: a VGG backbone convolutional neural network (CNN) as the front-end feature map extractor and a dual path multi-scale fusion networks as the back-end to generate density map. These du…
Video object detection is a tough task due to the deteriorated quality of video sequences captured under complex environments. Currently, this area is dominated by a series of feature enhancement based methods, which distill beneficial semantic information from multiple frames and generate enhanced features through fusing the distilled information. However, the distillation and fusion operations are usually performed at either frame level or instance level with external guidance using additional information, such as optical flow and feature memory. In this work, we propose a dual semantic fusi…
Road detection based on remote sensing images is of great significance to intelligent traffic management. The performances of the mainstream road detection methods are mainly determined by their extracted features, whose richness and robustness can be enhanced by fusing features of different types and cross-layer connections. However, the features in the existing mainstream model frameworks are often similar in the same layer by the single-task training, and the traditional cross-layer fusion ways are too simple to obtain an efficient effect, so more complex fusion ways besides concatenation a…
Nanjing University of Science and Technology（南京理工大学）
Change detection (CD) of remote sensing images is to detect the change region by analyzing the difference between two bitemporal images. It is extensively used in land resource planning, natural hazards monitoring and other fields. In our study, we propose a novel Siamese neural network for change detection task, namely Dual-UNet. In contrast to previous individually encoded the bitemporal images, we design an encoder differential-attention module to focus on the spatial difference relationships of pixels. In order to improve the generalization of networks, it computes the attention weights be…
D2-Mamba: Dual-Scale Fusion and Dual-Path Scanning with SSMs for Shadow Removal
School of Artificial Intelligence, Hebei University of Technology（人工智能学院，河北工业大学）；School of Electrical and Computer Engineering, University of Texas at Austin（电气与计算机工程学院，德克萨斯大学奥斯汀分校）；School of Health Sciences and Biomedical Engineering, Hebei University of Technology（健康科学与生物医学工程学院，河北工业大学）
Dr. B. C. Roy Engineering College（B.C.罗伊工程学院）；Indian Institute of Technology Kanpur（印度理工学院坎普尔分校）；Jadavpur University（jadavpur大学）
This paper proposes a robust ear identification system which is developed by fusing SIFT features of color segmented slice regions of an ear. The proposed ear identification method makes use of Gaussian mixture model (GMM) to build ear model with mixture of Gaussian using vector quantization algorithm and K-L divergence is applied to the GMM framework for recording the color similarity in the specified ranges by comparing color similarity between a pair of reference ear and probe ear. SIFT features are then detected and extracted from each color slice region as a part of invariant feature extr…
DualDiff: Dual-branch Diffusion Model for Autonomous Driving with Semantic Fusion
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence（国家级人机混合增强智能实验室）；National Engineering Research Center for Visual Information and Applications（国家视觉信息与应用工程研究中心）；Institute of Artificial Intelligence and Robotics（人工智能与机器人研究所）；Xi’an Jiaotong University（西安交通大学）；University of Science and Technology of China（中国科学技术大学）
With the rapid development of deep learning technology, more and more face forgeries by deepfake are widely spread on social media, causing serious social concern. Face forgery detection has become a research hotspot in recent years, and many related methods have been proposed until now. For those images with low quality and/or diverse sources, however, the detection performances of existing methods are still far from satisfactory. In this paper, we propose an improved Xception with dual attention mechanism and feature fusion for face forgery detection. Different from the middle flow in origin…
Electronic Information School, Wuhan University（武汉大学电子信息学院）；School of Cyber Science and Engineering, Wuhan University（武汉大学网络空间安全学院）
Anti-counterfeiting QR codes are widely used in people's work and life, especially in product packaging. However, the anti-counterfeiting QR code has the risk of being copied and forged in the circulation process. In reality, copying is usually based on genuine anti-counterfeiting QR codes, but the brands and models of copiers are diverse, and it is extremely difficult to determine which individual copier the forged anti-counterfeiting code come from. In response to the above problems, this paper proposes a method for copy forgery identification of anti-counterfeiting QR code based on deep lea…
MM-DETR: An Efficient Multimodal Detection Transformer with Mamba-Driven Dual-Granularity Fusion and Frequency-Aware Modality Adapters
School of Information and Electronics, Beijing Institute of Technology（信息与电子学院，北京理工大学）；Beijing Institute of Technology Chongqing Innovation Center（北京理工大学重庆创新中心）；National Key Laboratory for Space-Born Intelligent Information Processing（空间智能信息处理国家级重点实验室）；School of Automation, Beijing Institute of Technology（自动化学院，北京理工大学）
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）
Multilevel neural networks with dual-stage feature fusion for human activity recognition
Departamento de Informática, Universidad Carlos III de Madrid（信息学院，卡洛斯三世大学）；Departamento de Teoría de la Señal y Comunicaciones, Universidad Carlos III de Madrid（信号与通信理论系，卡洛斯三世大学）；CEIEC, Universidad Francisco de Vitoria（CEIEC，弗朗西斯科·维多利亚大学）
In recent years, rumors have had a devastating impact on society, making rumor detection a significant challenge. However, the studies on rumor detection ignore the intense emotions of images in the rumor content. This paper verifies that the image emotion improves the rumor detection efficiency. A Multimodal Dual Emotion feature in rumor detection, which consists of visual and textual emotions, is proposed. To the best of our knowledge, this is the first study which uses visual emotion in rumor detection. The experiments on real datasets verify that the proposed features outperform the state-…
DashFusion: Dual-stream Alignment with Hierarchical Bottleneck Fusion for Multimodal Sentiment Analysis
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；Beijing National Research Center for Information Science and Technology, Tsinghua University（清华大学信息科学与技术国家研究中心）；Department of Automation, Tsinghua University（清华大学自动化系）
The George Washington University（乔治华盛顿大学）；Children’s National Hospital（国家儿童医院）
Accurate nerve identification is critical during surgical procedures for preventing any damages to nerve tissues. Nerve injuries can lead to long-term detrimental effects for patients as well as financial overburdens. In this study, we develop a deep-learning network framework using the U-Net architecture with a Transformer block based fusion module at the bottleneck to identify nerve tissues from a multi-modal optical imaging system. By leveraging and extracting the feature maps of each modality independently and using each modalities information for cross-modal interactions, we aim to provid…
Shanghai AI Laboratory（上海人工智能实验室）；Uisee Research（驭势科技研究院）；ShanghaiTech University（上海科技大学）；Peking University（北京大学）；University of Pennsylvania（宾夕法尼亚大学）
Point cloud processing is a challenging task due to its sparsity and irregularity. Prior works introduce delicate designs on either local feature aggregator or global geometric architecture, but few combine both advantages. We propose Dual-Scale Point Cloud Recognition with High-frequency Fusion (DSPoint) to extract local-global features by concurrently operating on voxels and points. We reverse the conventional design of applying convolution on voxels and attention to points. Specifically, we disentangle point features through channel dimension for dual-scale processing: one by point-wise con…
DM-QPMNET: Dual-modality fusion network for cell segmentation in quantitative phase microscopy
College of Computing and Informatics, University of North Carolina at Charlotte, NC, USA（计算与信息学院，北卡罗来纳大学夏洛特分校）；Department of Physics and Optical Science, University of North Carolina at Charlotte, NC, USA（物理与光学科学系，北卡罗来纳大学夏洛特分校）；Center for TAIMing AI, University of North Carolina at Charlotte, NC, USA（TAIMing AI中心，北卡罗来纳大学夏洛特分校）
Single-photon emission computed tomography (SPECT) is a widely applied imaging approach for diagnosis of coronary artery diseases. Attenuation maps (u-maps) derived from computed tomography (CT) are utilized for attenuation correction (AC) to improve diagnostic accuracy of cardiac SPECT. However, SPECT and CT are obtained sequentially in clinical practice, which potentially induces misregistration between the two scans. Convolutional neural networks (CNN) are powerful tools for medical image registration. Previous CNN-based methods for cross-modality registration either directly concatenated t…
Clova AI Research, NAVER Corp（NAVER公司Clova AI研究中心）；Seoul National University（首尔大学）；SK T-brain；Surromind Robotics
We propose a video story question-answering (QA) architecture, Multimodal Dual Attention Memory (MDAM). The key idea is to use a dual attention mechanism with late fusion. MDAM uses self-attention to learn the latent concepts in scene frames and captions. Given a question, MDAM uses the second attention over these latent concepts. Multimodal fusion is performed after the dual attention processes (late fusion). Using this processing pipeline, MDAM learns to infer a high-level vision-language joint representation from an abstraction of the full video content. We evaluate MDAM on PororoQA and Mov…
