A systematic review on affective computing: Emotion models, databases, and recent advances
Affective computing conjoins the research topics of emotion recognition and sentiment
analysis, and can be realized with unimodal or multimodal data, consisting primarily of …
analysis, and can be realized with unimodal or multimodal data, consisting primarily of …
[HTML][HTML] A systematic survey on multimodal emotion recognition using learning algorithms
Emotion recognition is the process to detect, evaluate, interpret, and respond to people's
emotional states and emotions, ranging from happiness to fear to humiliation. The COVID-19 …
emotional states and emotions, ranging from happiness to fear to humiliation. The COVID-19 …
Bi-bimodal modality fusion for correlation-controlled multimodal sentiment analysis
Multimodal sentiment analysis aims to extract and integrate semantic information collected
from multiple modalities to recognize the expressed emotions and sentiment in multimodal …
from multiple modalities to recognize the expressed emotions and sentiment in multimodal …
Speech emotion recognition using self-supervised features
Self-supervised pre-trained features have consistently delivered state-of-art results in the
field of natural language processing (NLP); however, their merits in the field of speech …
field of natural language processing (NLP); however, their merits in the field of speech …
Emotions don't lie: An audio-visual deepfake detection method using affective cues
We present a learning-based method for detecting real and fake deepfake multimedia
content. To maximize information for learning, we extract and analyze the similarity between …
content. To maximize information for learning, we extract and analyze the similarity between …
Deep learning-based multimodal emotion recognition from audio, visual, and text modalities: A systematic review of recent advancements and future prospects
S Zhang, Y Yang, C Chen, X Zhang, Q Leng… - Expert Systems with …, 2023 - Elsevier
Emotion recognition has recently attracted extensive interest due to its significant
applications to human-computer interaction. The expression of human emotion depends on …
applications to human-computer interaction. The expression of human emotion depends on …
Former-dfer: Dynamic facial expression recognition transformer
This paper proposes a dynamic facial expression recognition transformer (Former-DFER) for
the in-the-wild scenario. Specifically, the proposed Former-DFER mainly consists of a …
the in-the-wild scenario. Specifically, the proposed Former-DFER mainly consists of a …
Mart: Masked affective representation learning via masked temporal distribution distillation
Limited training data is a long-standing problem for video emotion analysis (VEA). Existing
works leverage the power of large-scale image datasets for transferring while failing to …
works leverage the power of large-scale image datasets for transferring while failing to …
Target and source modality co-reinforcement for emotion understanding from asynchronous multimodal sequences
Perceiving human emotions from a multimodal perspective has received significant attention
in knowledge engineering communities. Due to the variable receiving frequency for …
in knowledge engineering communities. Due to the variable receiving frequency for …
Exploiting BERT for multimodal target sentiment classification through input space translation
Multimodal target/aspect sentiment classification combines multimodal sentiment analysis
and aspect/target sentiment classification. The goal of the task is to combine vision and …
and aspect/target sentiment classification. The goal of the task is to combine vision and …