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）
Infrared and visible image fusion aims to combine complementary information from both modalities to provide a more comprehensive scene understanding. However, due to the significant differences between the two modalities, preserving key features during the fusion process remains a challenge. To address this issue, we propose a dual-branch feature decomposition fusion network (DAF-Net) with domain adaptive, which introduces Multi-Kernel Maximum Mean Discrepancy (MK-MMD) into the base encoder and designs a hybrid kernel function suitable for infrared and visible image fusion. The base encoder built on the Restormer network captures global structural information while the detail encoder based on Invertible Neural Networks (INN) focuses on extracting detail texture information. By incorporating MK-MMD, the DAF-Net effectively aligns the latent feature spaces of visible and infrared images, thereby improving the quality of the fused images. Experimental results demonstrate that the proposed method outperforms existing techniques across multiple datasets, significantly enhancing both visual quality and fusion performance. The related Python code is available at https://github.com/xujian000/DAF-Net.
Suzhou University of Science and Technology（苏州科技大学）；Jiangnan University（江南大学）
Background: Leaning redundant and complementary relationships is a critical step in the human visual system. Inspired by the infrared cognition ability of crotalinae animals, we design a joint convolution auto-encoder (JCAE) network for infrared and visible image fusion. Methods: Our key insight is to feed infrared and visible pair images into the network simultaneously and separate an encoder stream into two private branches and one common branch, the private branch works for complementary features learning and the common branch does for redundant features learning. We also build two fusion r…
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…
MagicFuse: Single Image Fusion for Visual and Semantic Reinforcement
Electronic Information School, Wuhan University, China（武汉大学电子信息学院）；Suzhou Institute of Wuhan University, China（武汉大学苏州研究院）；School of Automation, Wuhan University, China（武汉大学自动化学院）
WMFormer++: Nested Transformer for Visible Watermark Removal via Implict Joint Learning
School of Software and Engineering, South China University of Technology（华南理工大学软件学院）；Peng Cheng Laboratory（鹏城实验室）；Tencent Wechat Department（腾讯微信事业群）；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机学院）；Xidian University（西安电子科技大学）
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）
A Semantic-Aware and Multi-Guided Network for Infrared-Visible Image Fusion
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University（教育部符号计算与知识工程重点实验室，吉林大学）；Institute of Digital Media, School of Electronic Engineering and Computer Science, Peking University（数字媒体研究所，电子工程与计算机科学学院，北京大学）
Current Injection Spiking Neural Network for Infrared and Visible Image Fusion
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；The Hong Kong Polytechnic University（香港理工大学）；Harbin Institute of Technology（哈尔滨工业大学）；State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University（北京大学计算机科学学院多媒体信息处理国家重点实验室）
MoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能教育部重点实验室）；INTSIG
Superimposing visible watermarks on images provides a powerful weapon to cope with the copyright issue. Watermark removal techniques, which can strengthen the robustness of visible watermarks in an adversarial way, have attracted increasing research interest. Modern watermark removal methods perform watermark localization and background restoration simultaneously, which could be viewed as a multi-task learning problem. However, existing approaches suffer from incomplete detected watermark and degraded texture quality of restored background. Therefore, we design a two-stage multi-task network t…
University of Science and Technology Beijing（北京科技大学）
State-of-the-art pedestrian detectors have achieved significant progress on non-occluded pedestrians, yet they are still struggling under heavy occlusions. The recent occlusion handling strategy of popular two-stage approaches is to build a two-branch architecture with the help of additional visible body annotations. Nonetheless, these methods still have some weaknesses. Either the two branches are trained independently with only score-level fusion, which cannot guarantee the detectors to learn robust enough pedestrian features. Or the attention mechanisms are exploited to only emphasize on th…
Amodal segmentation is a new direction of instance segmentation while considering the segmentation of the visible and occluded parts of the instance. The existing state-of-the-art method uses multi-task branches to predict the amodal part and the visible part separately and subtract the visible part from the amodal part to obtain the occluded part. However, the amodal part contains visible information. Therefore, the separated prediction method will generate duplicate information. Different from this method, we propose a method of amodal segmentation based on the idea of the jigsaw. The method…
Centre for Development of Advanced Computing（高级计算发展中心）
We first, introduce a deep learning based framework named as DeepIrisNet2 for visible spectrum and NIR Iris representation. The framework can work without classical iris normalization step or very accurate iris segmentation; allowing to work under non-ideal situation. The framework contains spatial transformer layers to handle deformation and supervision branches after certain intermediate layers to mitigate overfitting. In addition, we present a dual CNN iris segmentation pipeline comprising of a iris/pupil bounding boxes detection network and a semantic pixel-wise segmentation network. Furth…
Aligned Consensus Teaching for Label-Efficient Oriented Object Detection in Weakly-Aligned Visible-Infrared Imagery
Beijing University of Technology（北京工业大学）；University of Science and Technology of China（中国科学技术大学）；Peking University（北京大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Beijing Institute of Technology（北京理工大学）；Fuzhou University（福州大学）；China University of Geosciences Wuhan（中国地质大学（武汉））；Ghent University（根特大学）
Cross-Domain Transfer with Self-Supervised Spectral-Spatial Modeling for Hyperspectral Image Classification
Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences（泉州装备制造研究所、海西院、中国科学院）；Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences（福建物质结构研究所、中国科学院）；Liaoning Technical University（辽宁技术大学）；State Key Laboratory of Regional and Urban Ecology, Institute of Urban Environment, Chinese Academy of Sciences（区域与城市生态国家重点实验室、城市环境研究所、中国科学院）
CapHDR2IR: Caption-Driven Transfer from Visible Light to Infrared Domain
WMG, University of Warwick（华威大学制造工程学院）；School of Information Science and Technology, East China University of Science and Technology（华东理工大学信息科学与工程学院）；School of Engineering, Newcastle University（纽卡斯尔大学工程学院）
MTVDiff: Multimodal Conditional Latent Diffusion for Enhanced Thermal-to-Visible Face Translation
School of Computer Science and Engineering, Southeast University（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)（新一代人工智能技术及其交叉应用重点实验室（东南大学））；Monash University（莫纳什大学）
Stealthy in Semantics, Antagonistic in Space: Attacking Visible-Infrared Object Detectors via Object-Level Misalignment
Beijing University of Technology（北京工业大学）；Peking University（北京大学）；National Key Laboratory of Space Integrated Information System, Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所空间集成信息系统全国重点实验室）；Intelligent Science & Technology Academy of CASIC（中国航天科工集团智能科技研究院）
TDCNet: Transparent Objects Depth Completion with CNN-Transformer Dual-Branch Parallel Network
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences（中国科学院长春光学精密机械与物理研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Harbin Institute of Technology（哈尔滨工业大学）；College of Physics, Northeast Normal University（东北师范大学物理学院）
Identity Clue Refinement and Enhancement for Visible-Infrared Person Re-Identification
School of Computer Science, Nanjing University of Information Science and Technology（南京信息工程大学计算机学院）；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））；State Key Laboratory of Tibetan Intelligent Information Processing and Application, Qinghai Normal University（藏语智能信息处理与应用国家重点实验室（青海师范大学））
JRN-Geo: A Joint Perception Network based on RGB and Normal images for Cross-view Geo-localization
College of Information Science and Engineering, Northeastern University（信息科学与工程学院，东北大学）；School of Computer Science, University of Sheffield（计算机科学学院，谢菲尔德大学）
Bridging Knowledge Gap Between Image Inpainting and Large-Area Visible Watermark Removal
School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；Research Institute of Sun Yat-sen University in Shenzhen（中山大学深圳研究院）；Afirstsoft
Dual-branch Graph Feature Learning for NLOS Imaging
Zhejiang University（浙江大学）；China Mobile Research Institute（中国移动研究院）；Westlake University（西湖大学）；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）
University of Science and Technology of China（中国科学技术大学）；Microsoft Research Asia（微软亚洲研究院）
Occluded person re-identification (ReID) aims to match person images with occlusion. It is fundamentally challenging because of the serious occlusion which aggravates the misalignment problem between images. At the cost of incorporating a pose estimator, many works introduce pose information to alleviate the misalignment in both training and testing. To achieve high accuracy while preserving low inference complexity, we propose a network named Pose-Guided Feature Learning with Knowledge Distillation (PGFL-KD), where the pose information is exploited to regularize the learning of semantics alig…
MS-DETR: Multispectral Pedestrian Detection Transformer with Loosely Coupled Fusion and Modality-Balanced Optimization
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；Research Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）
