17,328 Results Found

Peer review is an essential part in the publication process, ensuring that Multimodal Technologies and Interaction maintains high quality standards for its published papers [...]

Peer review is the driving force of journal development, and reviewers are gatekeepers who ensure that Multimodal Technologies and Interaction maintains its standards for the high quality of its published papers [...]

The growing demand for personalized human–computer interaction calls for methods that jointly model emotional states and personality traits. However, large-scale multimodal corpora annotated for both tasks are still lacking. This challenge stem...

Background: Hemorrhagic stroke is a life-threatening cerebrovascular disease, and early identification is crucial for timely clinical intervention. Microwave imaging is non-ionizing, portable, and low-cost, and thus has potential for pre-hospital and...

Rigorous peer-reviews are the basis of high-quality academic publishing [...]

This conceptual review article explores the intersection of multimodal writing and multilingualism in a contemporary educational context, with a focus on both secondary and post-secondary classrooms. As digital tools, media platforms, and global comm...

Approximately 15–50% of patients with Crohn’s disease (CD) will require surgery within ten years following the diagnosis. The management of modifiable risk factors before surgery is essential to reduce postoperative complications and to p...

Multimodal Relation Extraction (MRE) is a core task for constructing Multimodal Knowledge images (MKGs). Most current research is based on fine-tuning small-scale single-modal image and text pre-trained models, but we find that image-text datasets fr...

Multimodal interaction is a transformative human-computer interaction (HCI) approach that allows users to interact with systems through various communication channels such as speech, gesture, touch, and gaze. With advancements in sensor technology an...

Multimodal analysis of human behavior has advanced rapidly, yet emotion recognition (ER), personality trait assessment (PTA), and ambivalence/hesitancy recognition (AHR) are usually reviewed separately. This review jointly examines these related but...

Multimodal sentiment analysis must combine textual, visual, and acoustic evidence whose reliability varies across utterances. We propose PGMCR, which uses a predicted three-way polarity distribution to compose sample-level modality-reliability weight...

Multitasking is pervasive in multimodal interaction, particularly within safety-critical domains like driving. Evaluating the impact of In-Vehicle Multimodal Interaction (IVMI) on drivers is critical, yet existing methods predominantly rely on post h...

Visual object tracking is a fundamental computer vision task recently extended to multimodal settings, where natural language descriptions complement visual information. Existing multimodal trackers typically rely on large-scale transformer architect...

The widespread emergence of multimodal data on social platforms has presented new opportunities for sentiment analysis. However, previous studies have often overlooked the issue of detail loss during modal interaction fusion. They also exhibit limita...

In this paper, a new model, known as the multimodal dynamic timetable model (DTM), is presented for computing optimal multimodal journeys in schedule-based public transport systems. The new model constitutes an extension of the dynamic timetable mode...

With the increasing growth of multimedia data on the Internet, multimodal image aesthetic assessment has attracted a great deal of attention in the image processing community. However, traditional multimodal methods often have the following two probl...

The rapid growth of information technology has led to an explosion of data, posing a significant challenge for data processing. Recommendation systems aim to address this by providing personalized content recommendations to users from vast datasets....

Multimodal interaction systems can provide users with natural and compelling interactive experiences. Despite the availability of various sensing devices, only some commercial multimodal applications are available. One reason may be the need for a mo...

Microbial foodborne pathogens present significant challenges to public health and the food industry, requiring rapid and accurate detection methods to prevent infections and ensure food safety. Conventional single biosensing techniques often exhibit...

Multimodal sensors capture and integrate diverse characteristics of a scene to maximize information gain. In optics, this may involve capturing intensity in specific spectra or polarization states to determine factors such as material properties or a...

Federated learning (FL), which provides a collaborative training scheme for distributed data sources with privacy concerns, has become a burgeoning and attractive research area. Most existing FL studies focus on taking unimodal data, such as image an...

As an essential part of artificial intelligence, a knowledge graph describes the real-world entities, concepts and their various semantic relationships in a structured way and has been gradually popularized in a variety practical scenarios. The major...

Emotion recognition has become one of the most researched subjects in the scientific community, especially in the human–computer interface field. Decades of scientific research have been conducted on unimodal emotion analysis, whereas recent co...

Multimodal learning analytics (MMLA), which has become increasingly popular, can help provide an accurate understanding of learning processes. However, it is still unclear how multimodal data is integrated into MMLA. By following the Preferred Report...

The growing demand for therapeutic monoclonal antibodies (mAbs) has heightened the need for efficient and scalable purification strategies. A major challenge in downstream processing is the removal of antibody aggregates, which can compromise drug sa...

This work reviews the state of the art in multimodal speech emotion recognition methodologies, focusing on audio, text and visual information. We provide a new, descriptive categorization of methods, based on the way they handle the inter-modality an...

Research on recommendation methods using multimodal graph information presents a significant challenge within the realm of information services. Prior studies in this area have lacked precision in the purification and denoising of multimodal informat...

For multimodal multi-objective optimization problems (MMOPs), there are multiple equivalent Pareto optimal solutions in the decision space that are corresponding to the same objective value. Therefore, the main tasks of multimodal multi-objective opt...

The detection of gas leakages is a crucial aspect to be considered in the chemical industries, coal mines, home applications, etc. Early detection and identification of the type of gas is required to avoid damage to human lives and the environment. T...

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...

Data processing in robotics is currently challenged by the effective building of multimodal and common representations. Tremendous volumes of raw data are available and their smart management is the core concept of multimodal learning in a new paradi...

Multimodal clustering achieves outstanding performance in various applications by aggregating information from heterogeneous devices. However, previous methods rely on strong-notion distances to fuse crossmodal complementary knowledge, established on...

Multimodal image registration is a difficult task, due to the significant intensity variations between the images. A common approach is to use sophisticated similarity measures, such as mutual information, that are robust to those intensity variation...

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...

With the increasing diversity of remote sensing modalities, multimodal image fusion improves target recognition accuracy but also introduces new security risks. Adversaries can inject small, imperceptible perturbations into a single modality to misle...

The integration of artificial intelligence into healthcare has advanced rapidly in recent years, with multimodal approaches emerging as promising tools for improving diagnostic accuracy and clinical decision making. These approaches combine heterogen...

Over the last few years, there has been an unprecedented proliferation of fake news. As a consequence, we are more susceptible to the pernicious impact that misinformation and disinformation spreading can have on different segments of our society. Th...

Machine Learning (ML) and Deep Learning (DL) are derivatives of Artificial Intelligence (AI) that have already demonstrated their effectiveness in a variety of domains, including healthcare, where they are now routinely integrated into patients&rsquo...

Multimodal summarization, a rapidly evolving field within multimodal learning, focuses on generating cohesive summaries by integrating information from diverse modalities, such as text and images. Unlike traditional unimodal summarization, multimodal...

In social interactions, people who are perceived as competent win more chances, tend to have more opportunities, and perform better in both personal and professional aspects of their lives. However, the process of evaluating competence is still poorl...

Multimodal learning analytics (MMLA) has become a prominent approach for capturing the complexity of learning by integrating diverse data sources such as video, audio, physiological signals, and digital interactions. This comprehensive review synthes...

Multimodal sentiment analysis, which aims to recognize the emotions expressed in multimodal data, has attracted extensive attention in both academia and industry. However, most of the current studies on user-generated reviews classify the overall sen...

Modern businesses are obligated to conform to regulations to prevent physical injuries and ill health for anyone present on a site under their responsibility, such as customers, employees and visitors. Safety officers (SOs) are engineers, who perform...

The widespread use of deep learning techniques for creating realistic synthetic media, commonly known as deepfakes, poses a significant threat to individuals, organizations, and society. As the malicious use of these data could lead to unpleasant sit...

The rapid adoption of multimodal learning systems across high-stakes applications has intensified concerns about fairness and social bias, yet existing research remains scattered across modalities, tasks, protected attributes, mitigation strategies,...

This article is concerned with digital, multimodal feedback that supports learning and assessment within education. Drawing on the research literature alongside a case study from a postgraduate program in digital education, I argue that approaching f...

The recent advancements in multimodal dialogue systems have been gaining importance in several domains such as retail, travel, fashion, among others. Several existing works have improved the understanding and generation of multimodal dialogues. Howev...

Nanomaterials have gained tremendous significance as contrast agents for both anatomical and functional preclinical bio-imaging. Contrary to conventional medical practices, molecular imaging plays an important role in exploring the affected cells, th...

Spam detection has been a topic of extensive research; however, there has been limited focus on multimodal spam detection. In this study, we introduce a novel approach for multilingual multimodal spam detection, presenting the Multilingual and Multim...

Multimodal named-entity recognition (MNER) aims to identify entity information by leveraging multimodal features. With recent research shifting to multi-image scenarios, existing methods overlook modality noise and lack effective cross-modal interact...