Context Based Emotion Recognition using EMOTIC Dataset

• URL: http://arxiv.org/abs/2003.13401v1
• Date: Mon, 30 Mar 2020 12:38:50 GMT
• Title: Context Based Emotion Recognition using EMOTIC Dataset
• Authors: Ronak Kosti, Jose M. Alvarez, Adria Recasens, Agata Lapedriza
• Abstract summary: We present EMOTIC, a dataset of images of people annotated with their apparent emotion. Using the EMOTIC dataset we train different CNN models for emotion recognition. Our results show how scene context provides important information to automatically recognize emotional states.
• Score: 22.631542327834595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In our everyday lives and social interactions we often try to perceive the emotional states of people. There has been a lot of research in providing machines with a similar capacity of recognizing emotions. From a computer vision perspective, most of the previous efforts have been focusing in analyzing the facial expressions and, in some cases, also the body pose. Some of these methods work remarkably well in specific settings. However, their performance is limited in natural, unconstrained environments. Psychological studies show that the scene context, in addition to facial expression and body pose, provides important information to our perception of people's emotions. However, the processing of the context for automatic emotion recognition has not been explored in depth, partly due to the lack of proper data. In this paper we present EMOTIC, a dataset of images of people in a diverse set of natural situations, annotated with their apparent emotion. The EMOTIC dataset combines two different types of emotion representation: (1) a set of 26 discrete categories, and (2) the continuous dimensions Valence, Arousal, and Dominance. We also present a detailed statistical and algorithmic analysis of the dataset along with annotators' agreement analysis. Using the EMOTIC dataset we train different CNN models for emotion recognition, combining the information of the bounding box containing the person with the contextual information extracted from the scene. Our results show how scene context provides important information to automatically recognize emotional states and motivate further research in this direction. Dataset and code is open-sourced and available at: https://github.com/rkosti/emotic and link for the peer-reviewed published article: https://ieeexplore.ieee.org/document/8713881

Related papers

• UniEmoX: Cross-modal Semantic-Guided Large-Scale Pretraining for Universal Scene Emotion Perception [8.54013419046987]
  We introduce UniEmoX, a cross-modal semantic-guided large-scale pretraining framework for visual emotion analysis. By exploiting the similarity between paired and unpaired image-text samples, UniEmoX distills rich semantic knowledge from the CLIP model to enhance emotional embedding representations. We develop a visual emotional dataset titled Emo8, covering nearly all common emotional scenes.
  arXiv  Detail & Related papers  (2024-09-27T16:12:51Z)
• Emotion Rendering for Conversational Speech Synthesis with Heterogeneous Graph-Based Context Modeling [50.99252242917458]
  Conversational Speech Synthesis (CSS) aims to accurately express an utterance with the appropriate prosody and emotional inflection within a conversational setting. To address the issue of data scarcity, we meticulously create emotional labels in terms of category and intensity. Our model outperforms the baseline models in understanding and rendering emotions.
  arXiv  Detail & Related papers  (2023-12-19T08:47:50Z)
• Multi-Branch Network for Imagery Emotion Prediction [4.618814297494939]
  We present a novel Multi-Branch Network (MBN) to predict both discrete and continuous emotions in an image. Our proposed method significantly outperforms state-of-the-art methods with 28.4% in mAP and 0.93 in MAE.
  arXiv  Detail & Related papers  (2023-12-12T18:34:56Z)
• EmoSet: A Large-scale Visual Emotion Dataset with Rich Attributes [53.95428298229396]
  We introduce EmoSet, the first large-scale visual emotion dataset annotated with rich attributes. EmoSet comprises 3.3 million images in total, with 118,102 of these images carefully labeled by human annotators. Motivated by psychological studies, in addition to emotion category, each image is also annotated with a set of describable emotion attributes.
  arXiv  Detail & Related papers  (2023-07-16T06:42:46Z)
• A cross-corpus study on speech emotion recognition [29.582678406878568]
  This study investigates whether information learnt from acted emotions is useful for detecting natural emotions. Four adult English datasets covering acted, elicited and natural emotions are considered. A state-of-the-art model is proposed to accurately investigate the degradation of performance.
  arXiv  Detail & Related papers  (2022-07-05T15:15:22Z)
• Multi-Cue Adaptive Emotion Recognition Network [4.570705738465714]
  We propose a new deep learning approach for emotion recognition based on adaptive multi-cues. We compare the proposed approach with the state-of-art approaches in the CAER-S dataset.
  arXiv  Detail & Related papers  (2021-11-03T15:08:55Z)
• SOLVER: Scene-Object Interrelated Visual Emotion Reasoning Network [83.27291945217424]
  We propose a novel Scene-Object interreLated Visual Emotion Reasoning network (SOLVER) to predict emotions from images. To mine the emotional relationships between distinct objects, we first build up an Emotion Graph based on semantic concepts and visual features. We also design a Scene-Object Fusion Module to integrate scenes and objects, which exploits scene features to guide the fusion process of object features with the proposed scene-based attention mechanism.
  arXiv  Detail & Related papers  (2021-10-24T02:41:41Z)
• Affective Image Content Analysis: Two Decades Review and New Perspectives [132.889649256384]
  We will comprehensively review the development of affective image content analysis (AICA) in the recent two decades. We will focus on the state-of-the-art methods with respect to three main challenges -- the affective gap, perception subjectivity, and label noise and absence. We discuss some challenges and promising research directions in the future, such as image content and context understanding, group emotion clustering, and viewer-image interaction.
  arXiv  Detail & Related papers  (2021-06-30T15:20:56Z)
• A Circular-Structured Representation for Visual Emotion Distribution Learning [82.89776298753661]
  We propose a well-grounded circular-structured representation to utilize the prior knowledge for visual emotion distribution learning. To be specific, we first construct an Emotion Circle to unify any emotional state within it. On the proposed Emotion Circle, each emotion distribution is represented with an emotion vector, which is defined with three attributes.
  arXiv  Detail & Related papers  (2021-06-23T14:53:27Z)
• Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causality [84.69595956853908]
  We present Affect2MM, a learning method for time-series emotion prediction for multimedia content. Our goal is to automatically capture the varying emotions depicted by characters in real-life human-centric situations and behaviors.
  arXiv  Detail & Related papers  (2021-03-11T09:07:25Z)

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.