FAF: A novel multimodal emotion recognition approach integrating face, body and text

• URL: http://arxiv.org/abs/2211.15425v1
• Date: Sun, 20 Nov 2022 14:43:36 GMT
• Title: FAF: A novel multimodal emotion recognition approach integrating face, body and text
• Authors: Zhongyu Fang, Aoyun He, Qihui Yu, Baopeng Gao, Weiping Ding, Tong Zhang, Lei Ma
• Abstract summary: We develop a large multimodal emotion dataset, named "HED" dataset, to facilitate the emotion recognition task. To promote recognition accuracy, "Feature After Feature" framework was used to explore crucial emotional information. We employ various benchmarks to evaluate the "HED" dataset and compare the performance with our method.
• Score: 13.485538135494153
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal emotion analysis performed better in emotion recognition depending on more comprehensive emotional clues and multimodal emotion dataset. In this paper, we developed a large multimodal emotion dataset, named "HED" dataset, to facilitate the emotion recognition task, and accordingly propose a multimodal emotion recognition method. To promote recognition accuracy, "Feature After Feature" framework was used to explore crucial emotional information from the aligned face, body and text samples. We employ various benchmarks to evaluate the "HED" dataset and compare the performance with our method. The results show that the five classification accuracy of the proposed multimodal fusion method is about 83.75%, and the performance is improved by 1.83%, 9.38%, and 21.62% respectively compared with that of individual modalities. The complementarity between each channel is effectively used to improve the performance of emotion recognition. We had also established a multimodal online emotion prediction platform, aiming to provide free emotion prediction to more users.

Related papers

• MEMO-Bench: A Multiple Benchmark for Text-to-Image and Multimodal Large Language Models on Human Emotion Analysis [53.012111671763776]
  This study introduces MEMO-Bench, a comprehensive benchmark consisting of 7,145 portraits, each depicting one of six different emotions. Results demonstrate that existing T2I models are more effective at generating positive emotions than negative ones. Although MLLMs show a certain degree of effectiveness in distinguishing and recognizing human emotions, they fall short of human-level accuracy.
  arXiv  Detail & Related papers  (2024-11-18T02:09:48Z)
• Smile upon the Face but Sadness in the Eyes: Emotion Recognition based on Facial Expressions and Eye Behaviors [63.194053817609024]
  We introduce eye behaviors as an important emotional cues for the creation of a new Eye-behavior-aided Multimodal Emotion Recognition dataset. For the first time, we provide annotations for both Emotion Recognition (ER) and Facial Expression Recognition (FER) in the EMER dataset. We specifically design a new EMERT architecture to concurrently enhance performance in both ER and FER.
  arXiv  Detail & Related papers  (2024-11-08T04:53:55Z)
• GCM-Net: Graph-enhanced Cross-Modal Infusion with a Metaheuristic-Driven Network for Video Sentiment and Emotion Analysis [2.012311338995539]
  This paper presents a novel framework that leverages the multi-modal contextual information from utterances and applies metaheuristic algorithms to learn for utterance-level sentiment and emotion prediction. To show the effectiveness of our approach, we have conducted extensive evaluations on three prominent multimodal benchmark datasets.
  arXiv  Detail & Related papers  (2024-10-02T10:07:48Z)
• EmoLLM: Multimodal Emotional Understanding Meets Large Language Models [61.179731667080326]
  Multi-modal large language models (MLLMs) have achieved remarkable performance on objective multimodal perception tasks. But their ability to interpret subjective, emotionally nuanced multimodal content remains largely unexplored. EmoLLM is a novel model for multimodal emotional understanding, incorporating with two core techniques.
  arXiv  Detail & Related papers  (2024-06-24T08:33:02Z)
• Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning [55.127202990679976]
  We introduce the MERR dataset, containing 28,618 coarse-grained and 4,487 fine-grained annotated samples across diverse emotional categories. This dataset enables models to learn from varied scenarios and generalize to real-world applications. We propose Emotion-LLaMA, a model that seamlessly integrates audio, visual, and textual inputs through emotion-specific encoders.
  arXiv  Detail & Related papers  (2024-06-17T03:01:22Z)
• Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations [15.705757672984662]
  Multimodal Emotion Recognition in Conversations (MERC) is a significant development direction for machine intelligence. Many data in MERC naturally exhibit an imbalanced distribution of emotion categories, and researchers ignore the negative impact of imbalanced data on emotion recognition. We propose the Class Boundary Enhanced Representation Learning (CBERL) model to address the imbalanced distribution of emotion categories in raw data. We have conducted extensive experiments on the IEMOCAP and MELD benchmark datasets, and the results show that CBERL has achieved a certain performance improvement in the effectiveness of emotion recognition.
  arXiv  Detail & Related papers  (2023-12-11T12:35:17Z)
• An Empirical Study and Improvement for Speech Emotion Recognition [22.250228893114066]
  Multimodal speech emotion recognition aims to detect speakers' emotions from audio and text. In this work, we consider a simple yet important problem: how to fuse audio and text modality information. Empirical results show our method obtained new state-of-the-art results on the IEMOCAP dataset.
  arXiv  Detail & Related papers  (2023-04-08T03:24:06Z)
• Mutilmodal Feature Extraction and Attention-based Fusion for Emotion Estimation in Videos [16.28109151595872]
  We introduce our submission to the CVPR 2023 Competition on Affective Behavior Analysis in-the-wild (ABAW) We exploited multimodal features extracted from video of different lengths from the competition dataset, including audio, pose and images. Our system achieves the performance of 0.361 on the validation dataset.
  arXiv  Detail & Related papers  (2023-03-18T14:08:06Z)
• Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models [53.31917090073727]
  We propose a neural network-based emotion recognition framework that uses a late fusion of transfer-learned and fine-tuned models from speech and text modalities. We evaluate the effectiveness of our proposed multimodal approach on the interactive emotional dyadic motion capture dataset.
  arXiv  Detail & Related papers  (2022-02-16T00:23:42Z)
• 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)

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.