Related papers: Multimodal Emotion Recognition and Sentiment Analy…
Multimodal emotion recognition (MER) is a fundamental complex research problem due to the uncertainty of human emotional expression and the heterogeneity gap between different modalities. Audio and text modalities are particularly important…
Speech emotion recognition (SER) remains a challenging yet crucial task due to the inherent complexity and diversity of human emotions. To address this problem, researchers attempt to fuse information from other modalities via multimodal…
Multimodal sentiment analysis in videos is a key task in many real-world applications, which usually requires integrating multimodal streams including visual, verbal and acoustic behaviors. To improve the robustness of multimodal fusion,…
Different from the emotion recognition in individual utterances, we propose a multimodal learning framework using relation and dependencies among the utterances for conversational emotion analysis. The attention mechanism is applied to the…
Automated emotion detection in speech is a challenging task due to the complex interdependence between words and the manner in which they are spoken. It is made more difficult by the available datasets; their small size and incompatible…
We propose a novel approach to multimodal sentiment analysis using deep neural networks combining visual analysis and natural language processing. Our goal is different than the standard sentiment analysis goal of predicting whether a…
The natural language processing and multimedia field has seen a notable surge in interest in multimodal sentiment recognition. Hence, this study aims to employ Target-Dependent Multimodal Sentiment Analysis (TDMSA) to identify the level of…
Multimodal sentiment analysis is a very actively growing field of research. A promising area of opportunity in this field is to improve the multimodal fusion mechanism. We present a novel feature fusion strategy that proceeds in a…
Related tasks often have inter-dependence on each other and perform better when solved in a joint framework. In this paper, we present a deep multi-task learning framework that jointly performs sentiment and emotion analysis both. The…
Multimodal Emotion Recognition (MER) focuses on identifying and interpreting emotions from modality-compound inputs. Closely mirroring human cognitive processes in real-world environments, MER has drawn substantial attention from both…
Multimodal Emotion Recognition (MER) aims to automatically identify and understand human emotional states by integrating information from various modalities. However, the scarcity of annotated multimodal data significantly hinders the…
Multimodal emotion understanding requires effective integration of text, audio, and visual modalities for both discrete emotion recognition and continuous sentiment analysis. We present EGMF, a unified framework combining expert-guided…
We compile baselines, along with dataset split, for multimodal sentiment analysis. In this paper, we explore three different deep-learning based architectures for multimodal sentiment classification, each improving upon the previous.…
Multimodal emotion recognition has attracted much attention recently. Fusing multiple modalities effectively with limited labeled data is a challenging task. Considering the success of pre-trained model and fine-grained nature of emotion…
Classifying group-level emotions is a challenging task due to complexity of video, in which not only visual, but also audio information should be taken into consideration. Existing works on multimodal emotion recognition are using bulky…
Emotion recognition has become an important field of research in the human-computer interactions domain. The latest advancements in the field show that combining visual with audio information lead to better results if compared to the case…
Emotion recognition is a critical task in human-computer interaction, enabling more intuitive and responsive systems. This study presents a multimodal emotion recognition system that combines low-level information from audio and text,…
Automatic emotion recognition is a hot topic with a wide range of applications. Much work has been done in the area of automatic emotion recognition in recent years. The focus has been mainly on using the characteristics of a person such as…
Emotional expressions form a key part of user behavior on today's digital platforms. While multimodal emotion recognition techniques are gaining research attention, there is a lack of deeper understanding on how visual and non-visual…
The main task of Multimodal Emotion Recognition in Conversations (MERC) is to identify the emotions in modalities, e.g., text, audio, image and video, which is a significant development direction for realizing machine intelligence. However,…