CAGE: Circumplex Affect Guided Expression Inference

• URL: http://arxiv.org/abs/2404.14975v1
• Date: Tue, 23 Apr 2024 12:30:17 GMT
• Title: CAGE: Circumplex Affect Guided Expression Inference
• Authors: Niklas Wagner, Felix Mätzler, Samed R. Vossberg, Helen Schneider, Svetlana Pavlitska, J. Marius Zöllner,
• Abstract summary: We present a comparative in-depth analysis of two common datasets (AffectNet and EMOTIC) equipped with the components of the circumplex model of affect. We propose a model for the prediction of facial expressions tailored for lightweight applications.
• Score: 9.108319009019912
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding emotions and expressions is a task of interest across multiple disciplines, especially for improving user experiences. Contrary to the common perception, it has been shown that emotions are not discrete entities but instead exist along a continuum. People understand discrete emotions differently due to a variety of factors, including cultural background, individual experiences, and cognitive biases. Therefore, most approaches to expression understanding, particularly those relying on discrete categories, are inherently biased. In this paper, we present a comparative in-depth analysis of two common datasets (AffectNet and EMOTIC) equipped with the components of the circumplex model of affect. Further, we propose a model for the prediction of facial expressions tailored for lightweight applications. Using a small-scaled MaxViT-based model architecture, we evaluate the impact of discrete expression category labels in training with the continuous valence and arousal labels. We show that considering valence and arousal in addition to discrete category labels helps to significantly improve expression inference. The proposed model outperforms the current state-of-the-art models on AffectNet, establishing it as the best-performing model for inferring valence and arousal achieving a 7% lower RMSE. Training scripts and trained weights to reproduce our results can be found here: https://github.com/wagner-niklas/CAGE_expression_inference.

Related papers

• Towards Context-Aware Emotion Recognition Debiasing from a Causal Demystification Perspective via De-confounded Training [14.450673163785094]
  Context-Aware Emotion Recognition (CAER) provides valuable semantic cues for recognizing the emotions of target persons. Current approaches invariably focus on designing sophisticated structures to extract perceptually critical representations from contexts. We present a Contextual Causal Intervention Module (CCIM) to de-confound the confounder.
  arXiv  Detail & Related papers  (2024-07-06T05:29:02Z)
• Modeling Emotional Trajectories in Written Stories Utilizing Transformers and Weakly-Supervised Learning [47.02027575768659]
  We introduce continuous valence and arousal labels for an existing dataset of children's stories originally annotated with discrete emotion categories. For predicting the thus obtained emotionality signals, we fine-tune a DeBERTa model and improve upon this baseline via a weakly supervised learning approach. A detailed analysis shows the extent to which the results vary depending on factors such as the author, the individual story, or the section within the story.
  arXiv  Detail & Related papers  (2024-06-04T12:17:16Z)
• Understanding Before Recommendation: Semantic Aspect-Aware Review Exploitation via Large Language Models [53.337728969143086]
  Recommendation systems harness user-item interactions like clicks and reviews to learn their representations. Previous studies improve recommendation accuracy and interpretability by modeling user preferences across various aspects and intents. We introduce a chain-based prompting approach to uncover semantic aspect-aware interactions.
  arXiv  Detail & Related papers  (2023-12-26T15:44:09Z)
• Unifying the Discrete and Continuous Emotion labels for Speech Emotion Recognition [28.881092401807894]
  In paralinguistic analysis for emotion detection from speech, emotions have been identified with discrete or dimensional (continuous-valued) labels. We propose a model to jointly predict continuous and discrete emotional attributes.
  arXiv  Detail & Related papers  (2022-10-29T16:12:31Z)
• Supervised Contrastive Learning for Affect Modelling [2.570570340104555]
  We introduce three different supervised contrastive learning approaches for training representations that consider affect information. Results demonstrate the representation capacity of contrastive learning and its efficiency in boosting the accuracy of affect models.
  arXiv  Detail & Related papers  (2022-08-25T17:40:19Z)
• Seeking Subjectivity in Visual Emotion Distribution Learning [93.96205258496697]
  Visual Emotion Analysis (VEA) aims to predict people's emotions towards different visual stimuli. Existing methods often predict visual emotion distribution in a unified network, neglecting the inherent subjectivity in its crowd voting process. We propose a novel textitSubjectivity Appraise-and-Match Network (SAMNet) to investigate the subjectivity in visual emotion distribution.
  arXiv  Detail & Related papers  (2022-07-25T02:20:03Z)
• Affect-DML: Context-Aware One-Shot Recognition of Human Affect using Deep Metric Learning [29.262204241732565]
  Existing methods assume that all emotions-of-interest are given a priori as annotated training examples. We conceptualize one-shot recognition of emotions in context -- a new problem aimed at recognizing human affect states in finer particle level from a single support sample. All variants of our model clearly outperform the random baseline, while leveraging the semantic scene context consistently improves the learnt representations.
  arXiv  Detail & Related papers  (2021-11-30T10:35:20Z)
• MEmoBERT: Pre-training Model with Prompt-based Learning for Multimodal Emotion Recognition [118.73025093045652]
  We propose a pre-training model textbfMEmoBERT for multimodal emotion recognition. Unlike the conventional "pre-train, finetune" paradigm, we propose a prompt-based method that reformulates the downstream emotion classification task as a masked text prediction. Our proposed MEmoBERT significantly enhances emotion recognition performance.
  arXiv  Detail & Related papers  (2021-10-27T09:57:00Z)
• Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition [31.40575057347465]
  This paper proposes a novel multi-task learning framework to recognize facial expressions in-the-wild. A shared feature representation is learned for both discrete and continuous recognition in a MTL setting. The results of our experiments show that our method outperforms the current state-of-the-art methods on discrete FER.
  arXiv  Detail & Related papers  (2021-06-07T10:20:05Z)
• Modality-Transferable Emotion Embeddings for Low-Resource Multimodal Emotion Recognition [55.44502358463217]
  We propose a modality-transferable model with emotion embeddings to tackle the aforementioned issues. Our model achieves state-of-the-art performance on most of the emotion categories. Our model also outperforms existing baselines in the zero-shot and few-shot scenarios for unseen emotions.
  arXiv  Detail & Related papers  (2020-09-21T06:10:39Z)
• Facial Expression Editing with Continuous Emotion Labels [76.36392210528105]
  Deep generative models have achieved impressive results in the field of automated facial expression editing. We propose a model that can be used to manipulate facial expressions in facial images according to continuous two-dimensional emotion labels.
  arXiv  Detail & Related papers  (2020-06-22T13:03:02Z)

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.