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Emotion Recognition

50 papers with code · Computer Vision

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Greatest papers with code

ExpNet: Landmark-Free, Deep, 3D Facial Expressions

2 Feb 2018fengju514/Expression-Net

Our ExpNet CNN is applied directly to the intensities of a face image and regresses a 29D vector of 3D expression coefficients.

3D FACIAL EXPRESSION RECOGNITION EMOTION RECOGNITION FACIAL LANDMARK DETECTION

Emotion Recognition in Conversations with Transfer Learning from Generative Conversation Modeling

11 Oct 2019SenticNet/conv-emotion

Given the large amount of available conversational data, we investigate whether generative conversational models can be leveraged to transfer affective knowledge for the target task of detecting emotions in context.

EMOTION RECOGNITION IN CONVERSATION TRANSFER LEARNING

Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances

8 May 2019SenticNet/conv-emotion

Emotion is intrinsic to humans and consequently emotion understanding is a key part of human-like artificial intelligence (AI).

EMOTION RECOGNITION IN CONVERSATION

Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors

23 Nov 2018A2Zadeh/CMU-MultimodalSDK

Humans convey their intentions through the usage of both verbal and nonverbal behaviors during face-to-face communication.

EMOTION RECOGNITION MULTIMODAL SENTIMENT ANALYSIS

MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations

ACL 2019 SenticNet/MELD

We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations.

DIALOGUE GENERATION EMOTION RECOGNITION

DepecheMood++: a Bilingual Emotion Lexicon Built Through Simple Yet Powerful Techniques

8 Oct 2018marcoguerini/DepecheMood

Several lexica for sentiment analysis have been developed and made available in the NLP community.

EMOTION RECOGNITION SENTIMENT ANALYSIS

End-to-End Multimodal Emotion Recognition using Deep Neural Networks

27 Apr 2017tzirakis/Multimodal-Emotion-Recognition

The system is then trained in an end-to-end fashion where - by also taking advantage of the correlations of the each of the streams - we manage to significantly outperform the traditional approaches based on auditory and visual handcrafted features for the prediction of spontaneous and natural emotions on the RECOLA database of the AVEC 2016 research challenge on emotion recognition.

MULTIMODAL EMOTION RECOGNITION