Emotion Recognition is an important area of research to enable effective human-computer interaction. Human emotions can be detected using speech signal, facial expressions, body language, and electroencephalography (EEG). Source: Using Deep Autoencoders for Facial Expression Recognition
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In this paper, we apply multiscale area attention in a deep convolutional neural network to attend emotional characteristics with varied granularities and therefore the classifier can benefit from an ensemble of attentions with different scales.
In this paper, we address the task of utterance level emotion recognition in conversations using commonsense knowledge.
Ranked #1 on Emotion Recognition in Conversation on EmoryNLP
We propose an approach, TL-ERC, where we pre-train a hierarchical dialogue model on multi-turn conversations (source) and then transfer its parameters to a conversational emotion classifier (target).
Ranked #6 on Emotion Recognition in Conversation on DailyDialog
Emotion recognition in conversation (ERC) has received much attention, lately, from researchers due to its potential widespread applications in diverse areas, such as health-care, education, and human resources.
Ranked #1 on Emotion Recognition in Conversation on SEMAINE
Emotion is intrinsic to humans and consequently emotion understanding is a key part of human-like artificial intelligence (AI).
Ranked #5 on Emotion Recognition in Conversation on EC
Emotion recognition in conversations is crucial for building empathetic machines.
Ranked #3 on Emotion Recognition in Conversation on SEMAINE
Emotion recognition in conversations is crucial for the development of empathetic machines.
Ranked #5 on Emotion Recognition in Conversation on SEMAINE
Our ExpNet CNN is applied directly to the intensities of a face image and regresses a 29D vector of 3D expression coefficients.
Ranked #1 on 3D Facial Expression Recognition on 2017_test set (using extra training data)