Image captioning plays a crucial role in aligning visual content with natural language, serving as a key step toward effective cross-modal understanding. Transformer has become the dominant language model in image captioning. Existing Transformer-bas...
Multi-view information fusion can provide more accurate, complete and reliable data descriptions for monitoring objects, effectively improve the limitations and unreliability of single-view data. Existing multi-view information fusion based on deep l...
Due to the presence of regions with weak textures or non-Lambertian surfaces, feature matching in learning-based Multi-View Stereo (MVS) algorithms often leads to incorrect matches, resulting in the construction of the flawed cost volume and incomple...
The near-future parking space availability is informative for the formulation of parking-related policy in urban areas. Plenty of studies have contributed to the spatial–temporal prediction for parking occupancy by considering the adjacency bet...
Multi-view 3D reconstruction aims to discover 3D characteristics based on visual information captured across multiple viewpoints. Transformer networks have shown remarkable success in various computer vision tasks, including multi-view 3D reconstruct...
Humans typically make decisions based on past experiences and observations, while in the field of robotic manipulation, the robot’s action prediction often relies solely on current observations, which tends to make robots overlook environmental...
Traditional 3D reconstruction models have consistently faced the challenge of balancing high recall of object edges with maintaining a high precision. In this paper, we introduce a post-processing method, the Multi-Head Attention Refiner (MA-R), desi...
To improve the accuracy of emotion recognition, this paper proposes a multi-view EEG-fNIRS and cross-attention fusion module named FGCN-TCNN-CAF, which employs a differentiated modeling strategy for the frequency, spatial, and temporal features of EE...
Concomitant with the recent advances in deep learning, automatic speech recognition and visual speech recognition (VSR) have received considerable attention. However, although VSR systems must identify speech from both frontal and profile faces in re...
The Inter-SubNet speechenhancement network improves subband interaction by enabling the exchange of complementary information across frequency bands, ensuring robust feature refinement while significantly reducing computational load through lightweig...
Regional epidemic risk prediction requires holistic modeling of heterogeneous data sources such as demographic structure, health capacity, geographical features and human mobility. In this study, a unique and multi-modal epidemiological data set inte...
Photoplethysmography (PPG)-based blood glucose estimation is attractive for non-invasive monitoring, but its development is constrained by limited paired PPG–glucose data and the weak representation of glucose-related waveform variations. This...
Visual dialog is a challenging vision-language task in which a series of questions visually grounded by a given image are answered. To resolve the visual dialog task, a high-level understanding of various multimodal inputs (e.g., question, dialog his...
Satellite-Drone Image Cross-View Geolocalization has wide applications. Due to the pronounced variations in the visual features of 3D objects under different angles, Satellite-Drone cross-view image geolocalization remains an unresolved challenge. Th...
Fringe projection profilometry (FPP) is widely used for 3D reconstruction, but conventional single-view FPP systems suffer from inherent occlusions and shadow regions, leading to incomplete surface recovery. In this study, we propose CVA-Net, an end-...
The widespread dissemination of fake news across different domains exerts a negative impact on social order. Current fake news detection models face two major challenges. First, the issue of domain shift constrains the generalization capability of mo...
Multi-view Synthetic Aperture Radar (SAR) provides rich information for target recognition. However, fusing features from unaligned multi-view images presents challenges for existing methods. Conventional early fusion methods often rely on image regi...
Research on electroencephalogram-based motor imagery (MI-EEG) can identify the limbs of subjects that generate motor imagination by decoding EEG signals, which is an important issue in the field of brain–computer interface (BCI). Existing deep-...
Internal disease in asphalt pavement is a crucial indicator of pavement health and serves as a vital basis for maintenance and rehabilitation decisions. It is closely related to the optimization and allocation of funds by highway maintenance manageme...
This paper presents reliable estimation of deterioration levels via late fusion using multi-view distress images for practical inspection. The proposed method simultaneously solves the following two problems that are necessary to support the practica...
The key to intelligent traffic control and guidance lies in accurate prediction of traffic flow. Since traffic flow data is nonlinear, complex, and dynamic, in order to overcome these issues, graph neural network techniques are employed to address th...
The cross-lingual text classification task remains a long-standing challenge that aims to train a classifier on high-resource source languages and apply it to classify texts in low-resource target languages, bridging linguistic gaps while maintaining...
Current single-view image inpainting methods often suffer from low image information utilization and suboptimal repair outcomes. To address these challenges, this paper introduces a novel image inpainting framework that leverages a structure-guided m...
Session-based recommendation (SBR) aims to predict the next item a user may interact with based on an anonymous session, playing a crucial role in real-time recommendation scenarios. However, existing SBR models struggle to effectively capture local...
Bearing fault diagnosis is critical for the reliability and safety of rotating machinery in industrial applications. Traditional fault diagnosis methods and single-view deep learning models often fail to capture the complex, multi-dimensional nature...
Current multi-target multi-camera tracking algorithms demand increased requirements for re-identification accuracy and tracking reliability. This study proposed an improved end-to-end multi-target tracking algorithm that adapts to multi-view multi-sc...
Accurate sheep detection supports counting, tracking, behavior analysis, and health monitoring in intelligent livestock farming. However, occlusion, scale variation, nighttime low light, and background interference can cause missed detections and ina...
This paper proposes SpecBEV, an enhanced multi-view 3D object detection framework for autonomous driving using bird’s-eye-view (BEV) representations. Compared with LiDAR-based methods, multi-camera perception offers higher cost-effectiveness an...
Recommender systems are now integral to many online platforms, including e-commerce, social media, and content streaming services. However, their widespread use also exposes them to significant security threats. One of the most critical is the shilli...
Multi-view stereo methods utilize image sequences from different views to generate a 3D point cloud model of the scene. However, existing approaches often overlook coarse-stage features, impacting the final reconstruction accuracy. Moreover, using a...
Early detection is crucial for the survival and recovery of lung cancer patients. Computer-aided diagnosis system can assist in the early diagnosis of lung cancer by providing decision support. While deep learning methods are increasingly being appli...
Person re-identification (re-ID) aims to retrieve images of a given individual from different camera views. Obstacles obstructing parts of a pedestrian’s body often result in incomplete identity information, impairing recognition performance. T...
Background: The detection of lung nodules at their early stages may significantly enhance the survival rate and prevent progression to severe disability caused by advanced lung cancer, but it often requires manual and laborious efforts for radiologis...
Graph convolutional networks (GCNs) are a promising approach for addressing the necessity for long-range information in hyperspectral image (HSI) classification. Researchers have attempted to develop classification methods that combine strong general...
Timely and reliable mapping of landslide-affected areas from high-spatial-resolution optical imagery is essential for disaster investigation and post-event assessment. However, this task remains challenging because landslides usually exhibit large-sc...
Defect detection in power scenarios is a critical task that plays a significant role in ensuring the safety, reliability, and efficiency of power systems. The existing technology requires enhancement in its learning ability from large volumes of data...
Twitter, as a popular social network, has been targeted by different bot attacks. Detecting social bots is a challenging task, due to their evolving capacity to avoid detection. Extensive research efforts have proposed different techniques and approa...
Speech separation, sometimes known as the “cocktail party problem”, is the process of separating individual speech signals from an audio mixture that includes ambient noises and several speakers. The goal is to extract the target speech i...
Precisely identifying and delineating food regions automatically from images, a task known as food image segmentation, is crucial for enabling applications in food science such as automated dietary logging, accurate nutritional analysis, and food saf...
The precise classification of breast cancer subtypes is crucial for clinical diagnosis and treatment, yet early symptoms are often subtle. The use of multi-omics data from high-throughput sequencing can improve the classification accuracy. However, m...
With the accelerated progress of computational aesthetics and digital interior design, the demand for real-time and precise detection of aesthetic objects on edge devices has become increasingly pressing in applications such as intelligent design ass...
Since side-effects of drugs are one of the primary reasons for their failure in clinical trials, predicting their side-effects can help reduce drug development costs. We proposed a method based on heterogeneous graph transformer and capsule networks...
Accurate station-level numerical weather predictions are critical for disaster prevention and mitigation, with error correction playing an essential role. However, existing correction models struggle to effectively handle the high-dimensional feature...
Accurate identification of potential drug–target interactions (DTIs) is a crucial task in drug development and repositioning. Despite the remarkable progress achieved in recent years, improving the performance of DTI prediction still presents s...
A growing body of experimental evidence suggests that microRNAs (miRNAs) are closely associated with specific human diseases and play critical roles in their development and progression. Therefore, identifying miRNA related to specific diseases is of...
Accurate identification of disease-associated microRNAs (miRNAs) is crucial for elucidating pathogenic mechanisms and advancing therapeutic discovery. Although computational methods, particularly those based on biological networks, have become essent...
With the rapid advancement of smart forestry, 3D reconstruction and the extraction of structural parameters have emerged as indispensable tools in modern forest monitoring. Although traditional methods involving LiDAR and manual surveys remain effect...
Diabetic retinopathy (DR), a critical ocular disease that can lead to blindness, demands early and accurate diagnosis to prevent vision loss. Current automated DR diagnosis methods face two core challenges: first, subtle early lesions such as microan...
Railway bearings experience speed- and load-dependent distribution shifts that complicate fault diagnosis. This study proposes a Physics-Guided Multi-View Transformer with Block Attention Residuals (PGMV-BAR). Characteristic-order prior tokens sequen...
Identifying core features is a crucial step in design patent infringement assessments (DPIAs), as the identification results serve as a vital basis for subsequent qualitative and quantitative assessments. Traditional DPIAs typically rely on the exper...
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