With the industrial demand caused by multi-sensor image fusion, infrared and visible image fusion (IVIF) technology is flourishing. In recent years, scale decomposition methods have led the trend for feature extraction. Such methods, however, have lo...
Magnetic resonance (MR) imaging is widely used in the clinical field due to its non-invasiveness, but the long scanning time is still a bottleneck for its popularization. Using the complementary information between multi-modal imaging to accelerate i...
Light field (LF) cameras can record multi-view images from a single scene, and these images can provide spatial and angular information to improve the performance of image super-resolution (SR). However, it is a challenge to incorporate distinctive i...
Different action recognition tasks exhibit significant variations in their reliance on local versus global features. Particularly for long-video understanding, dynamically balancing the contributions of both has become a critical challenge for improv...
Concrete dams are essential components in hydraulic engineering, and the detection of surface cracks remains a major concern in structural health monitoring. This study presents CCT Net, a segmentation model designed to enable the automated detection...
Remote-sensing images constitute an important means of obtaining geographic information. Image super-resolution reconstruction techniques are effective methods of improving the spatial resolution of remote-sensing images. Super-resolution reconstruct...
Accurate segmentation of lung nodules from pulmonary computed tomography (CT) slices plays a vital role in the analysis and diagnosis of lung cancer. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in the automatic seg...
We present a new architecture to address the challenges of speaker identification that arise in interaction of humans with social robots. Though deep learning systems have led to impressive performance in many speech applications, limited speech data...
Camouflaged object detection (COD) seeks to precisely detect and delineate objects that are concealed within complex and ambiguous backgrounds. However, due to subtle texture variations and semantic ambiguity, it remains a highly challenging task. Ex...
The main purpose of infrared and visible image fusion is to produce a fusion image that incorporates less redundant information while incorporating more complementary information, thereby facilitating subsequent high-level visual tasks. However, obta...
To enhance the comprehensive representation capability and fusion accuracy of remote sensing information, this paper proposes a multi-resolution hierarchical fusion network (MRLF) tailored to the heterogeneous characteristics of optical and synthetic...
Compared to other multi-source image fusion tasks, visible and SAR image fusion faces a lack of training data in deep learning-based methods. Introducing structural priors to design fusion networks is a viable solution. We incorporated the feature hi...
Infrared and visible image fusion can integrate rich edge details and salient infrared targets, resulting in high-quality images suitable for advanced tasks. However, most available algorithms struggle to fully extract detailed features and overlook...
Image fusion is the generation of an informative image that contains complementary information from the original sensor images, such as texture details and attentional targets. Existing methods have designed a variety of feature extraction algorithms...
The fusion-based classification of hyperspectral (HS) and light detection and ranging (LiDAR) images has become a prominent research topic, as their complementary information can effectively improve classification performance. The current methods enc...
Currently, the detection of blueberry internal bruising focuses mostly on single hyperspectral imaging (HSI) systems. Attempts to fuse different HSI systems with complementary spectral ranges are still lacking. A push broom based HSI system and a liq...
The purpose of multi-modal visual information fusion is to integrate the data of multi-sensors to generate an image with higher quality, more information, and greater clarity so that it contains more complementary information and fewer redundant feat...
Unimodal brain–computer interfaces (BCIs) often suffer from inherent limitations due to the characteristic of using single modalities. While hybrid BCIs combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) of...
The rapid development of unmanned aerial vehicles (UAVs) and deep learning has accelerated the application of semantic segmentation in precision agriculture (SSPA). A key driver of this progress lies in multimodal fusion, which leverages complementar...
Multimodal automatic modulation recognition (MAMR) has emerged as a prominent research area. The effective fusion of features from different modalities is crucial for MAMR tasks. An effective multimodal fusion mechanism should maximize the extraction...
The tracking community is increasingly focused on RGBT tracking, which leverages the complementary strengths of corresponding visible light and thermal infrared images. The most well-known RGBT trackers, however, are unable to balance performance and...
Currently, multi-modal 3D object detection methods have become a key area of research in the field of autonomous driving. Fusion is an essential factor affecting performance in multi-modal object detection. However, previous methods still suffer from...
Pedestrian detection from unmanned aerial vehicles (UAVs) holds significant value in security surveillance and emergency response applications. While visible-infrared (RGB-IR) fusion technology demonstrates potential in handling complex lighting cond...
The fusion of synthetic aperture radar (SAR) and visible images offers complementary spatial and spectral information, enabling more reliable and comprehensive scene interpretation. However, SAR speckle noise and the intrinsic modality gap pose signi...
Automated extraction of buildings from earth observation (EO) data has long been a fundamental but challenging research topic. Combining data from different modalities (e.g., high-resolution imagery (HRI) and light detection and ranging (LiDAR) data)...
The infrared and visible image fusion task aims to generate a single image that preserves complementary features and reduces redundant information from different modalities. Although convolutional neural networks (CNNs) can effectively extract local...
Multi-focus image fusion is an important method used to combine the focused parts from source multi-focus images into a single full-focus image. Currently, to address the problem of multi-focus image fusion, the key is on how to accurately detect the...
RGB-T salient object detection (SOD) has made significant progress in recent years. However, most existing works are based on heavy models, which are not applicable to mobile devices. Additionally, there is still room for improvement in the design of...
With the popularity of social networking services, people are increasingly inclined to share their opinions and feelings on social networks, leading to the rapid increase in multimodal posts on various platforms. Therefore, multimodal sentiment analy...
Since the mid-1990s, X-ray phase contrast imaging (XPCI) has attracted increasing interest in the industrial and bioimaging fields due to its high sensitivity to weakly absorbing materials and has gained widespread acceptance. XPCI can simultaneously...
No-reference stereoscopic image quality assessment (NR-SIQA) remains a fundamental challenge due to the complex biological mechanisms of binocular rivalry and fusion, particularly under asymmetric distortions. In this paper, we propose a novel framew...
Infrared and visible image fusion aims to produce an informative fused image for the same scene by integrating the complementary information from two source images. Most deep-learning-based fusion networks utilize small kernel-size convolution to ext...
Due to the unique imaging mechanism of SAR, targets in SAR images present complex scattering characteristics. As a result, intelligent target detection in SAR images has been facing many challenges, which mainly lie in the insufficient exploitation o...
The current deep learning-based image fusion methods can not sufficiently learn the features of images in a wide frequency range. Therefore, we proposed IFormerFusion, which is based on the Inception Transformer and cross-domain frequency fusion. To...
RGB-D salient object detection (SOD) aims at locating the most eye-catching object in visual input by fusing complementary information of RGB modality and depth modality. Most of the existing RGB-D SOD methods integrate multi-modal features to genera...
Multispectral pedestrian detection, which consists of a color stream and thermal stream, is essential under conditions of insufficient illumination because the fusion of the two streams can provide complementary information for detecting pedestrians...
Object detection is an important perception task in autonomous driving and advanced driver assistance systems. The visible camera is widely used for perception, but its performance is limited by illumination and environmental variations. For robust v...
The fusion tracking of RGB and thermal infrared image (RGBT) is paid wide attention to due to their complementary advantages. Currently, most algorithms obtain modality weights through attention mechanisms to integrate multi-modalities information. T...
Multi-frame super-resolution (MFSR) leverages complementary information between image sequences of the same scene to increase the resolution of the reconstructed image. As a branch of MFSR, burst super-resolution aims to restore image details by leve...
The accurate identification of rice fields through remote sensing is critical for agricultural monitoring and global food security. While optical and Synthetic Aperture Radar (SAR) data offer complementary advantages for crop mapping—spectral r...
Although multimodal feature fusion technology in fake news detection can integrate complementary information from different modal data, the semantic inconsistency of multimodal features will lead to feature fusion difficulties. And there is the probl...
Infrared and visible image fusion integrates complementary information from different modalities into a single image, providing sufficient imaging information for scene interpretation and downstream target recognition tasks. However, existing fusion...
With the rapid advancement of remote sensing technologies, the joint classification of hyperspectral image (HSI) and LiDAR data has become a key research focus in the field. To address the impact of inherent uncertainties in hyperspectral images on c...
The fusion of Raman and visible–near-infrared (VIS–NIR) spectroscopy provides a promising pathway for rapid and non-destructive soil analysis. However, conventional fusion strategies often fail to properly balance modality discrepancies a...
Infrared–visible image fusion is valuable across various applications due to the complementary information that it provides. However, the current fusion methods face challenges in achieving high-quality fused images. This paper identifies a lim...
Precise identification of active jamming in complex electromagnetic environments remains critically challenging for cognitive radar systems. Current methods often exhibit limitations in insufficient feature extraction and underutilization of jamming...
RGB-IR object detection provides a promising solution for complex scenarios, such as remote sensing and low-light environments, by leveraging the complementary strengths of visible and infrared modalities. Despite significant advancements, two key ch...
To tackle the challenges of relative attitude adaptability and limited sample availability in underwater moving target recognition for active sonar, this study focuses on key aspects such as feature extraction, network model design, and information f...
In the field of remote sensing image applications, RGB and infrared image object detection is an important technology. The object detection performance can be improved and the robustness of the algorithm will be enhanced by making full use of their c...
Accurate detection of individual tree crowns is a critical prerequisite for precisely extracting forest structural parameters, which is vital for forestry resources monitoring. While unmanned aerial vehicle (UAV)-acquired RGB imagery, combined with d...
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