Learning Graph Representation of Person-specific Cognitive Processes from Audio-visual Behaviours for Automatic Personality Recognition

• URL: http://arxiv.org/abs/2110.13570v2
• Date: Wed, 27 Oct 2021 10:14:58 GMT
• Title: Learning Graph Representation of Person-specific Cognitive Processes from Audio-visual Behaviours for Automatic Personality Recognition
• Authors: Siyang Song, Zilong Shao, Shashank Jaiswal, Linlin Shen, Michel Valstar and Hatice Gunes
• Abstract summary: We propose to represent the target subjects person-specific cognition in the form of a person-specific CNN architecture. Each person-specific CNN is explored by the Neural Architecture Search (NAS) and a novel adaptive loss function. Experimental results show that the produced graph representations are well associated with target subjects' personality traits.
• Score: 17.428626029689653
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: This approach builds on two following findings in cognitive science: (i) human cognition partially determines expressed behaviour and is directly linked to true personality traits; and (ii) in dyadic interactions individuals' nonverbal behaviours are influenced by their conversational partner behaviours. In this context, we hypothesise that during a dyadic interaction, a target subject's facial reactions are driven by two main factors, i.e. their internal (person-specific) cognitive process, and the externalised nonverbal behaviours of their conversational partner. Consequently, we propose to represent the target subjects (defined as the listener) person-specific cognition in the form of a person-specific CNN architecture that has unique architectural parameters and depth, which takes audio-visual non-verbal cues displayed by the conversational partner (defined as the speaker) as input, and is able to reproduce the target subject's facial reactions. Each person-specific CNN is explored by the Neural Architecture Search (NAS) and a novel adaptive loss function, which is then represented as a graph representation for recognising the target subject's true personality. Experimental results not only show that the produced graph representations are well associated with target subjects' personality traits in both human-human and human-machine interaction scenarios, and outperform the existing approaches with significant advantages, but also demonstrate that the proposed novel strategies such as adaptive loss, and the end-to-end vertices/edges feature learning, help the proposed approach in learning more reliable personality representations.

Related papers

• Visual-Geometric Collaborative Guidance for Affordance Learning [63.038406948791454]
  We propose a visual-geometric collaborative guided affordance learning network that incorporates visual and geometric cues. Our method outperforms the representative models regarding objective metrics and visual quality.
  arXiv  Detail & Related papers  (2024-10-15T07:35:51Z)
• How Do You Perceive My Face? Recognizing Facial Expressions in Multi-Modal Context by Modeling Mental Representations [5.895694050664867]
  We introduce a novel approach for facial expression classification that goes beyond simple classification tasks. Our model accurately classifies a perceived face and synthesizes the corresponding mental representation perceived by a human when observing a face in context. We evaluate synthesized expressions in a human study, showing that our model effectively produces approximations of human mental representations.
  arXiv  Detail & Related papers  (2024-09-04T09:32:40Z)
• Nonverbal Interaction Detection [83.40522919429337]
  This work addresses a new challenge of understanding human nonverbal interaction in social contexts. We contribute a novel large-scale dataset, called NVI, which is meticulously annotated to include bounding boxes for humans and corresponding social groups. Second, we establish a new task NVI-DET for nonverbal interaction detection, which is formalized as identifying triplets in the form individual, group, interaction> from images. Third, we propose a nonverbal interaction detection hypergraph (NVI-DEHR), a new approach that explicitly models high-order nonverbal interactions using hypergraphs.
  arXiv  Detail & Related papers  (2024-07-11T02:14:06Z)
• Affective-NLI: Towards Accurate and Interpretable Personality Recognition in Conversation [30.820334868031537]
  Personality Recognition in Conversation (PRC) aims to identify the personality traits of speakers through textual dialogue content. We propose Affective Natural Language Inference (Affective-NLI) for accurate and interpretable PRC.
  arXiv  Detail & Related papers  (2024-04-03T09:14:24Z)
• Disentangled Interaction Representation for One-Stage Human-Object Interaction Detection [70.96299509159981]
  Human-Object Interaction (HOI) detection is a core task for human-centric image understanding. Recent one-stage methods adopt a transformer decoder to collect image-wide cues that are useful for interaction prediction. Traditional two-stage methods benefit significantly from their ability to compose interaction features in a disentangled and explainable manner.
  arXiv  Detail & Related papers  (2023-12-04T08:02:59Z)
• Learning Person-specific Network Representation for Apparent Personality Traits Recognition [3.19935268158731]
  We propose to recognize apparent personality recognition approach which first trains a person-specific network for each subject. We then encode the weights of the person-specific network to a graph representation, as the personality representation for the subject. The experimental results show that our novel network weights-based approach achieved superior performance than most traditional latent feature-based approaches.
  arXiv  Detail & Related papers  (2023-03-01T06:10:39Z)
• CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition [80.07590100872548]
  We propose Contrastive Inhibitory Adaptati On (CIAO), a mechanism that adapts the last layer of facial encoders to depict specific affective characteristics on different datasets. CIAO presents an improvement in facial expression recognition performance over six different datasets with very unique affective representations.
  arXiv  Detail & Related papers  (2022-08-10T15:46:05Z)
• Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey [71.43956423427397]
  We aim to identify the nonverbal cues and computational methodologies resulting in effective performance. This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings. Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
  arXiv  Detail & Related papers  (2022-07-20T13:37:57Z)
• Leveraging Recent Advances in Deep Learning for Audio-Visual Emotion Recognition [2.1485350418225244]
  Spontaneous multi-modal emotion recognition has been extensively studied for human behavior analysis. We propose a new deep learning-based approach for audio-visual emotion recognition.
  arXiv  Detail & Related papers  (2021-03-16T15:49:15Z)
• You Impress Me: Dialogue Generation via Mutual Persona Perception [62.89449096369027]
  The research in cognitive science suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose P2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding.
  arXiv  Detail & Related papers  (2020-04-11T12:51:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.