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Advancements in Multimodal Sentiment Analysis (MSA) have predominantly focused on leveraging the interdependence of text, acoustic, and visual modalities to enhance sentiment prediction. However, efficiently integrating these modalities remains a ...

Stress detection in real-world settings presents significant challenges due to the complexity of human emotional expression influenced by biological, psychological, and social factors. While traditional methods like EEG, ECG, and EDA sensors provide ...

Machine Learning (ML) for Mineral Prospectivity Mapping (MPM) remains a challenging problem as it requires the analysis of associations between large-scale multi-modal geospatial data and a few historical mineral commodity observations (positive labels). ...

The MER-NOISE challenges participants to classify emotions from multimodal data, specifically audio and visual, with added noise. In this paper, we present a solution for the NOISE track of the MER2024 competition, which focuses on the robustness of ...

The 5th Multimodal Sentiment Analysis Challenge (MuSe), a workshop in conjunction with ACM Multimedia '24, is focused on Multimodal Machine Learning in the domain of Affective Computing. Two different sub-challenges are proposed: Social Perception Sub-...

The parallel audio-visual-text data contains vast amount of information. Thus it is essential to develop machine learning algorithms that can utilise them efficiently. In this work, we investigated unimodal and multimodal solutions for MuSe Humor and ...

The Multimodal Sentiment Analysis Challenge (MuSe) 2024 addresses two contemporary multimodal affect and sentiment analysis problems: In the Social Perception Sub-Challenge (MuSe-Perception), participants will predict 16 different social attributes of ...

In this paper, we present our unique method for the MuSe 2024 Perception sub-challenge. In the Perception sub-challenge, 21 labeled social perceptions data are given, 16 social perceptions are required to be predicted. Joint learning is crucial for our ...

Social perception is a crucial psychological concept that explains how we understand and interpret others and their behaviors. It encompasses the complex process of discerning individual characteristics, intentions, and emotions, significantly ...

Automatic social perception recognition is a new task to mimic the measurement of human traits, which was previously done by humans via questionnaires. We evaluated unimodal and multimodal systems to predict agentive and communal traits from the LMU-ELP ...

Link prediction aims to infer missing valid triplets to complete knowledge graphs, with recent inclusion of multimodal information to enrich entity representations. Existing methods project multimodal information into a unified embedding space or learn ...

Automatic Speech Recognition (ASR) models pre-trained on large-scale speech datasets have achieved significant breakthroughs compared with traditional methods. However, mainstream pre-trained ASR models encounter challenges in distinguishing homophones, ...

Multimodal Entity Linking (MEL) aims to address the ambiguity in multimodal mentions and associate them with Multimodal Knowledge Graphs (MMKGs). Existing works primarily focus on designing multimodal interaction and fusion mechanisms to enhance the ...

Multimodal emotion recognition in conversation (MERC) seeks to identify the speakers' emotions expressed in each utterance, offering significant potential across diverse fields. The challenge of MERC lies in balancing speaker modeling and context ...

Complaints are pivotal expressions within e-commerce communication, yet the intricate nuances of human interaction present formidable challenges for AI agents to grasp comprehensively. While recent attention has been drawn to analyzing complaints within ...

Multimodal Sentiment Analysis (MSA) has witnessed remarkable progress and gained increasing attention in recent decade. However, current MSA methodologies primarily rely on global representations extracted from different modalities, such as the mean of ...

Incorporating domain-specific visual information into text poses one of the critical challenges for domain-specific multi-modal neural machine translation (DMNMT). While most existing DMNMT methods often borrow multi-modal fusion frameworks from multi-...

Stance detection, which aims to identify public opinion towards specific targets using social media data, is an important yet challenging task. With the proliferation of diverse multimodal social media content including text, and images multimodal stance ...

Medical report generation aims at automating the synthesis of accurate and comprehensive diagnostic reports from radiological images. The task can significantly enhance clinical decision-making and alleviate the workload on radiologists. Existing works ...

The recent advancements in cross-modal transformers have demonstrated their superior performance in RGB-D segmentation tasks by effectively integrating information from both RGB and depth modalities. However, existing methods often overlook the varying ...