The accurate measurement of cable force plays an important role in the structural form, structural behavior, and safety evaluation of large-span cable-stayed bridges. A fast cable-force estimation method is proposed based on the alignment recognition...
Text recognition is a rapidly evolving task with broad practical applications across multiple industries. However, due to the arbitrary-shape text arrangement, irregular text font, and unintended occlusion of font, this remains a challenging task. To...
To enhance the crypticity and operational efficiency of unmanned underwater vehicle (UUV) charging, we propose an automatic alignment method for an underwater charging platform based on monocular vision recognition. This method accurately identifies...
Radar emitter recognition based on pulse description words (PDWs) serves as a fundamental prerequisite for target identification and tracking in electronic support measures (ESM). Although self-supervised learning (SSL) has been widely applied to tex...
With the continuous progress in drone and materials technology, numerous bionic drones have been developed and employed in various fields. These bionic drones are designed to mimic the shape of birds, seamlessly blending into the natural environment...
The accuracy of the wearable inertia-measurement-unit (IMU)-sensor-based gesture recognition may be significantly affected by undesired changes in the body-fixed frame and the sensor-fixed frame according to the change in the subject and the sensor a...
Accurately identifying tool wear states with variable cutting parameters can improve machining quality and efficiency. However, existing wear state recognition methods based on unsupervised domain adaptation mostly employ the knowledge transfer learn...
Existing person re-recognition (Re-ID) methods usually suffer from poor generalization capability and over-fitting problems caused by insufficient training samples. We find that high-level attributes, semantic information, and part-based local inform...
Three-dimensional (3D) face recognition has become a trending research direction in both industry and academia. However, traditional facial recognition methods carry high computational costs and face data storage costs. Deep learning has led to a sig...
Face recognition has been comprehensively studied. However, face recognition in the wild still suffers from unconstrained face directions. Frontal face synthesis is a popular solution, but some facial features are missed after synthesis. This paper p...
Traffic sign recognition is a fundamental perception task in intelligent transportation and autonomous driving systems. Despite the strong performance of modern deep neural networks under matched training and testing conditions, their recognition acc...
Background/Objectives: Adversarial domain adaptation methods are widely used in EEG-based emotion recognition to reduce the influence of individual differences and the non-stationary characteristics of electroencephalogram (EEG) signals. Most existin...
Scene Text Recognition (STR) faces significant challenges under complex degradation conditions, such as distortion, occlusion, and semantic ambiguity. Most existing methods rely heavily on language priors for correction, but effectively constructing...
Graph neural networks (GNNs) are extensively utilized to capture the spatial–temporal relationships among human body parts for skeleton-based action recognition. However, due to the inefficient information propagation caused by redundant sampli...
Background: Integrating human neural signals with computational vision systems offers a promising route toward more robust visual recognition, yet supporting mixed-granularity recognition, where both coarse- and fine-grained categories must be distin...
In this paper, we present a novel approach for text-independent phone-to-audio alignment based on phoneme recognition, representation learning and knowledge transfer. Our method leverages a self-supervised model (Wav2Vec2) fine-tuned for phoneme reco...
Heterogeneous face recognition (HFR) has aroused significant interest in recent years, with some challenging tasks such as misalignment problems and limited HFR data. Misalignment occurs among different modalities’ images mainly because of misa...
Deep learning models have received widespread attention in pathological image classification and recognition tasks. However, their performance relies on large amounts of annotated data, which are difficult to obtain for pathological images, severely...
In this paper, a self-alignment method for strapdown inertial navigation systems based on the q-method is studied. In addition, an improved method based on integrating gravitational apparent motion to form apparent velocity is designed, which can red...
Manual transcription of music is a tedious work, which can be greatly facilitated by optical music recognition (OMR) software. However, OMR software is error prone in particular for older handwritten documents. This paper introduces and evaluates a p...
The Chang’e-6 mission achieved the first successful sample collection and return from the Moon’s far side. Accurate alignment detection of the primary packaging container is critical for the success of this mission, as it ensures proper r...
In the field of numerical control machining, tool alignment technology is a key link to ensure machining accuracy and quality. Tool alignment refers to determining the correct position of the tool relative to the workpiece, and its accuracy directly...
As a sub-challenge of EmotiW (the Emotion Recognition in the Wild challenge), how to improve performance on the AFEW (Acted Facial Expressions in the wild) dataset is a popular benchmark for emotion recognition tasks with various constraints, includi...
Cross-domain named entity recognition (NER) is a crucial task in various practical applications, particularly when faced with the challenge of limited data availability in target domains. Existing methodologies primarily depend on feature representat...
Many cell functions in all living organisms rely on protein-based molecular recognition involving disorder-to-order transitions upon binding by molecular recognition features (MoRFs). A well accepted computational tool for identifying likely protein-...
Visible-infrared person re-identification aims to solve the matching problem between cross-camera and cross-modal person images. Existing methods strive to perform better cross-modal alignment, but often neglect the critical importance of feature enh...
With the increasing content richness of social media platforms, Multimodal Named Entity Recognition (MNER) faces the dual challenges of heterogeneous feature fusion and accurate entity recognition. Aiming at the key problems of inconsistent distribut...
Recent advances in large multimodal models (LMMs) have opened new opportunities for multitask recognition from remote sensing images. However, existing approaches still face challenges in effectively recognizing the complex geospatial characteristics...
Multimodal amed entity recognition (MNER) is a natural language-processing technique that integrates text and visual modalities to detect and segment entity boundaries and their types from unstructured multimodal data. Although existing methods allev...
Multimodal emotion recognition has emerged as a prominent field in affective computing, offering superior performance compared to single-modality methods. Among various physiological signals, EEG signals and EOG data are highly valued for their compl...
Deep learning-based models have shown to be one of the main active research topics in emotion recognition systems from Electroencephalogram (EEG) signals. However, a significant challenge is to effectively recognize new emotions that are incorporated...
We introduce an integrated method for processing depth maps measured by a laser profile sensor. It serves for the recognition and alignment of an object given by a single example. Firstly, we look for potential object contours, mainly using the Retin...
With the continuous expansion of power grids and the advancement of ultra-high voltage (UHV) projects, transmission lines are increasingly traversing areas characterized by micro-terrain. These localized topographic features can intensify meteorologi...
This paper presents the development of a hybrid feature—dorsal hand vein and dorsal geometry—modality for human recognition. Our proposed hybrid feature extraction method exploits two types of features: dorsal hand geometric-related and l...
While sign language combines sequential hand motions with concurrent non-manual cues (e.g., mouth shapes and head tilts), current recognition systems lack multimodal annotation methods capable of capturing their hierarchical semantics. To bridge this...
The dynamic time warping (DTW) algorithm is widely used in pattern matching and sequence alignment tasks, including speech recognition and time series clustering. However, DTW algorithms perform poorly when aligning sequences of uneven sampling frequ...
Radar-based human behavior recognition has significant value in IoT application scenarios such as smart healthcare and intelligent security. However, the existing unimodal perception architecture is susceptible to multipath effects, which can lead to...
This paper proposes a novel multimodal emotion recognition framework, termed Sparse Alignment and Liquid-Mamba (SALM), which effectively integrates the complementary strengths of Mamba networks and Liquid Neural Networks (LNNs). To capture neural dyn...
Zero-shot SAR ship recognition aims to identify unseen ship categories without annotated SAR samples, offering a promising solution for open-category maritime remote sensing. Although synthetic aperture radar (SAR) provides all-weather and day-and-ni...
Mechanical properties are critical for characterizing and fabricating advanced materials. While current characterization methods are well-established for the nanoscale and larger millimeter-scale, a significant gap exists in automated testing at the...
Few-shot multimodal sentiment analysis (FMSA) has garnered substantial attention due to the proliferation of multimedia applications, especially given the frequent difficulty in obtaining large quantities of training samples. Previous works have dire...
The orientation of plate images in license plate recognition is one of the factors that influence its accuracy. In particular, tilted plate images are harder to detect and recognize characters with than aligned ones. To this end, the rectification of...
Zero-shot learning (ZSL) enables the recognition of unseen categories by leveraging models trained only on labeled seen-class samples in the source domain. Traditional ZSL methods typically assume identical data distributions across source and target...
The creation of large-area, unintentional-defect-free three-dimensional (3D) photonic crystals in the optical regime is a key challenge toward the realization of the arbitrary 3D manipulation of photons. In this article, we discuss an advanced fabric...
The crux of information systems is efficient storage and access to useful data by users. This paper is an overview of work that has advanced the use of such systems in recent years, primarily in machine learning, and specifically, deep learning metho...
As the development of low-altitude economies and aerial countermeasures continues, the safety of unmanned aerial vehicles becomes increasingly critical, making emitter identification in remote sensing practices more essential. Effective recognition o...
Synthetic aperture radar (SAR) images often suffer from coupled degradations such as speckle noise, background clutter, and system disturbances, which distort target structure and reduce feature discriminability for target recognition. Most existing...
A piping and instrumentation diagram (P&ID) is a key drawing widely used in the energy industry. In a digital P&ID, all included objects are classified and made amenable to computerized data management. However, despite being widespread, a la...
Soybean diseases are the main factors causing serious yield reduction in soybeans. Therefore, an automatic identification method for soybean leaf diseases is an urgent need to develop. However, most soybean leaf disease recognition models have proble...
Face anti-spoofing is critical for enhancing the robustness of face recognition systems against presentation attacks. Existing methods predominantly rely on binary classification tasks. Recently, methods based on domain generalization have yielded pr...
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