Modality differences and intra-class differences have been hot research problems in the field of cross-modality person re-identification currently. In this paper, we propose a cross-modality person re-identification method based on joint middle modal...
In order to minimize the disparity between visible and infrared modalities and enhance pedestrian feature representation, a cross-modality person re-identification method is proposed, which integrates modality generation and feature enhancement. Spec...
Emotion recognition has been gaining attention in recent years due to its applications on artificial agents. To achieve a good performance with this task, much research has been conducted on the multi-modality emotion recognition model for leveraging...
Multi-modal object tracking (MMOT) has received widespread attention for the ability to overcome single-sensor perception limitations. However, existing methods encounter several critical challenges. Representation learning and generalization capabil...
In this paper, we produce an extension of Englebretsen’s line diagrams in order to represent modal syllogistic, i.e., we add some diagrammatic objects and rules to his system in order to reason about modal syllogistics in a diagrammatic, linear...
In dense crowds and complex electromagnetic environments of metro stations, UWB-based seamless payment suffers from limited positioning accuracy and insufficient stability. A promising solution is to incorporate the vision modality, thereby enhancing...
This paper investigates the modal and non-modal uses and readings of the purposive suffix in the Western Desert (Pama-Nyungan) language Pintupi-Luritja. It is shown that the suffix is associated with a range of root-modal readings, with some variabil...
Multi-modal knowledge graph completion (MMKGC) aims to complete knowledge graphs by integrating structural information with multi-modal (e.g., visual, textual, and numerical) features and leveraging cross-modal reasoning within a unified semantic spa...
Multi-modal anti-spoofing aims to differentiate live users from spoofing attacks using multiple biometric modalities during model training. While existing anti-spoofing methods often incorporate just one biometric modality, the effectiveness of attac...
Integrating multi-modal data in natural language processing has opened new pathways for the enhancement of dialogue summarization. However, existing models often struggle to effectively synthesize textual, auditory, and visual inputs. This paper intr...
Recently, multimodal approaches that combine various modalities have been attracting attention to recognizing emotions more accurately. Although multimodal fusion delivers strong performance, it is computationally intensive and difficult to handle in...
In communication, people adjust their information expression based on the audience’s attitude toward a topic, which is known as the audience-tuning effect. This effect also leads individuals to develop memory biases favoring the audience’...
Hypertrophic cardiomyopathy (HCM) is characterized by abnormal growth of the myocardium with myofilament disarray and myocardial hyper-contractility, leading to left ventricular hypertrophy and fibrosis. Where culprit genes are identified, they typic...
Background/Objectives: Accurate automatic segmentation of multimodal magnetic resonance imaging (MRI) is essential for neurosurgical planning and image-guided procedures. However, existing three-dimensional segmentation models often struggle with low...
Background/Objectives: Verbs are considered to be more abstract than nouns, as they represent actions, states, and events, which are less tangible, more flexible in their meaning and thus less univocally specified. It has been suggested that children...
Multimodal fusion networks play a pivotal role in leveraging diverse sources of information for enhanced machine learning applications in aerial imagery. However, current approaches often suffer from a bias towards certain modalities, diminishing the...
With the rapid development of applications such as edge computing, the Internet of Things (IoT), and embodied intelligence, massive multimodal data are continuously generated on end devices in a streaming manner. To maintain model adaptability and ro...
Radiological departments face challenges in efficiency and diagnostic consistency. The interpretation of radiographs remains highly variable between practitioners, which creates potential disparities in patient care. This study explores how artificia...
In classification tasks, such as face recognition and emotion recognition, multimodal information is used for accurate classification. Once a multimodal classification model is trained with a set of modalities, it estimates the class label by using t...
Cross-modality person re-identification faces challenges such as illumination discrepancies, local occlusions, and inconsistent modality structures, leading to misalignment and sensitivity issues. We propose GLCN, a framework that addresses these pro...
With the increasing adoption of teledermatology, there is a need to improve the automatic organization of medical records, being dermatological image modality a key filter in this process. Although there has been considerable effort in the classifica...
Person re-identification is the technique of identifying the same person in different camera shots, known as ReID for short. Most existing models focus on single-modality person re-identification involving only visible images. However, the visible mo...
Medical images are valuable for clinical diagnosis and decision making. Image modality is an important primary step, as it is capable of aiding clinicians to access required medical image in retrieval systems. Traditional methods of modality classifi...
In harsh weather conditions, the infrared modality can supplement or even replace the visible modality. However, the lack of a large-scale dataset for infrared features hinders the generation of a robust pre-training model. Most existing infrared obj...
In the history of deaf education, questions attending communication, language, and modality have generated much discussion, and even heated debate. This should not be surprising as these questions touch on a fundamental issue that is central to polic...
This paper presents a novel multi-modal segment anything model (MSAM) for glioma tumor segmentation using structural MRI images and diffusion tensor imaging data. We designed an effective multimodal feature fusion block to effectively integrate featu...
Cross-modal remote sensing object detection holds significant potential for around-the-clock applications. However, the modality differences between cross-modal data and the degradation of feature quality under adverse weather conditions limit detect...
Axiomatically, symmetry is a fundamental property of mathematical functions defining similarity measures, where similarity measures are important tools in many areas of computer science, including machine learning and image processing. In this paper,...
The 6D pose estimation using RGBD images plays a pivotal role in robotics applications. At present, after obtaining the RGB and depth modality information, most methods directly concatenate them without considering information interactions. This lead...
Today, medical images play a crucial role in obtaining relevant medical information for clinical purposes. However, the quality of medical images must be analyzed and improved. Various factors affect the quality of medical images at the time of medic...
Background/Objectives: Working memory (WM) performance relies on the coordination of updating and inhibition functions within the central executive system. However, their interaction under varying cognitive loads, particularly across sensory modaliti...
The rapid growth of online data has made it very convenient for people to obtain information. However, it also leads to the problem of “information overload”. Therefore, how to detect hot events from the massive amount of information has...
This study investigated the impacts of different notification modalities used in low and high ambient sound environments for mobile phone interaction. Three different notification modalities—Shaking Visual, Shaking Visual + Vibration, and Vibration—w...
28 October 2024
In this paper, we present a novel model that enhances performance by extending the dual-modality TEVAD model—originally leveraging visual and textual information—into a multi-modal framework that integrates visual, audio, and textual data...
This paper explores the non-indicative modal meanings that the classical indicative plural inflection -ūn and the preverbal particle b- express in different varieties of Arabic. Moreover, it argues that these two forms allow the speaker to intro...
Multispectral information fusion technology is a practical approach to enhance pedestrian detection performance in low light conditions. However, current methods often overlook the impact of illumination on modal weights and the significance of inter...
Multimodal sentiment analysis aims to utilize multisource information such as text, speech and vision to more comprehensively and accurately identify an individual’s emotional state. However, existing methods still face challenges in practical...
Since attention mechanism was introduced in neural machine translation, attention has been combined with the long short-term memory (LSTM) or replaced the LSTM in a transformer model to overcome the sequence-to-sequence (seq2seq) problems with the LS...
Background/Objectives: Sensory experiences and emotional information contribute to conceptual knowledge. Compared to exteroceptive modality (e.g., visual, auditory), interoceptive modality predominates in the representation of emotional concepts. How...
Due to the great success of Vision Transformer (ViT) in image classification tasks, many pure Transformer architectures for human action recognition have been proposed. However, very few works have attempted to use Transformer to conduct bimodal acti...
An image reconstruction method is proposed based on Lagrange-Newton method for electrical impedance tomography (EIT) and ultrasound tomography (UT) dual-modality imaging. Since the change in conductivity distribution is usually accompanied with the c...
This study investigates how control modalities and recognition accuracy influence construction workers’ trust and acceptance of collaborative robots. Sixty participants evaluated voice and gesture control under varying levels of recognition acc...
The objective of this article is to study the problem of pedestrian classification across different light spectrum domains (visible and far-infrared (FIR)) and modalities (intensity, depth and motion). In recent years, there has been a number of appr...
Multimodal Sentiment Analysis (MSA) integrates multiple modalities to better understand human emotions. However, existing methods often neglect heterogeneity among modal features, causing redundancy and inconsistencies. Additionally, the dynamic inte...
Indoor-scene semantic segmentation is of great significance to indoor navigation, high-precision map creation, route planning, etc. However, incorporating RGB and HHA images for indoor-scene semantic segmentation is a promising yet challenging task,...
RGB-T tracking involves the use of images from both visible and thermal modalities. The primary objective is to adaptively leverage the relatively dominant modality in varying conditions to achieve more robust tracking compared to single-modality tra...
Aerial object detection plays a critical role in numerous fields, utilizing the flexibility of airborne platforms to achieve real-time tasks. Combining visible and infrared sensors can overcome limitations under low-light conditions, enabling full-ti...
Accessibility-oriented sign language recognition plays an important role in intelligent human–computer interaction, but cross-subject recognition remains challenging because test signers are unseen during training. Variations in appearance, mot...
Robots have been proposed as intelligent technology that can support the independent living and health of older adults. While significant advances are being made regarding hardware and intelligent software to support autonomous actions of robots, les...
The segmentation of medical images, particularly for brain tumors, is essential for clinical diagnosis and treatment planning. In this study, we proposed MMAformer, a Multiscale Modality-Aware Transformer model, which is designed for segmenting brain...
of 507