Multimodal classification: Current landscape, taxonomy and future directions
Multimodal classification research has been gaining popularity with new datasets in
domains such as satellite imagery, biometrics, and medicine. Prior research has shown the …
domains such as satellite imagery, biometrics, and medicine. Prior research has shown the …
A review on methods and applications in multimodal deep learning
Deep Learning has implemented a wide range of applications and has become increasingly
popular in recent years. The goal of multimodal deep learning (MMDL) is to create models …
popular in recent years. The goal of multimodal deep learning (MMDL) is to create models …
Recent advances and trends in multimodal deep learning: A review
Deep Learning has implemented a wide range of applications and has become increasingly
popular in recent years. The goal of multimodal deep learning is to create models that can …
popular in recent years. The goal of multimodal deep learning is to create models that can …
Privacy enhanced multimodal neural representations for emotion recognition
M Jaiswal, EM Provost - Proceedings of the AAAI Conference on …, 2020 - ojs.aaai.org
Many mobile applications and virtual conversational agents now aim to recognize and adapt
to emotions. To enable this, data are transmitted from users' devices and stored on central …
to emotions. To enable this, data are transmitted from users' devices and stored on central …
Speaker-invariant adversarial domain adaptation for emotion recognition
Automatic emotion recognition methods are sensitive to the variations across different
datasets and their performance drops when evaluated across corpora. We can apply …
datasets and their performance drops when evaluated across corpora. We can apply …
Muse: a multimodal dataset of stressed emotion
Endowing automated agents with the ability to provide support, entertainment and
interaction with human beings requires sensing of the users' affective state. These affective …
interaction with human beings requires sensing of the users' affective state. These affective …
[PDF][PDF] Mind the gap: On the value of silence representations to lexical-based speech emotion recognition.
Speech timing and non-speech regions (here referred to as “silence”), often play a critical
role in the perception of spoken language. Silence represents an important paralinguistic …
role in the perception of spoken language. Silence represents an important paralinguistic …
Emotion Recognition in the Real-World: Passively Collecting and Estimating Emotions from Natural Speech Data of Individuals with Bipolar Disorder
Emotions provide critical information regarding a person's health and well-being. Therefore,
the ability to track emotion and patterns in emotion over time could provide new …
the ability to track emotion and patterns in emotion over time could provide new …
X-Norm: Exchanging Normalization Parameters for Bimodal Fusion
Multimodal learning aims to process and relate information from different modalities to
enhance the model's capacity for perception. Current multimodal fusion mechanisms either …
enhance the model's capacity for perception. Current multimodal fusion mechanisms either …
Fine-Grained Emotion Comprehension: Semisupervised Multimodal Emotion and Intensity Recognition
Z Fang, Z Liu, T Liu, CC Hung - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The rapid advancement of deep learning and the exponential growth of multimodal data
have led to increased attention on multimodal emotion analysis and comprehension in affect …
have led to increased attention on multimodal emotion analysis and comprehension in affect …