MEMO-Bench: A Multiple Benchmark for Text-to-Image and Multimodal Large Language Models on Human Emotion Analysis

• URL: http://arxiv.org/abs/2411.11235v1
• Date: Mon, 18 Nov 2024 02:09:48 GMT
• Title: MEMO-Bench: A Multiple Benchmark for Text-to-Image and Multimodal Large Language Models on Human Emotion Analysis
• Authors: Yingjie Zhou, Zicheng Zhang, Jiezhang Cao, Jun Jia, Yanwei Jiang, Farong Wen, Xiaohong Liu, Xiongkuo Min, Guangtao Zhai,
• Abstract summary: 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.
• Score: 53.012111671763776
• License:
• Abstract: Artificial Intelligence (AI) has demonstrated significant capabilities in various fields, and in areas such as human-computer interaction (HCI), embodied intelligence, and the design and animation of virtual digital humans, both practitioners and users are increasingly concerned with AI's ability to understand and express emotion. Consequently, the question of whether AI can accurately interpret human emotions remains a critical challenge. To date, two primary classes of AI models have been involved in human emotion analysis: generative models and Multimodal Large Language Models (MLLMs). To assess the emotional capabilities of these two classes of models, this study introduces MEMO-Bench, a comprehensive benchmark consisting of 7,145 portraits, each depicting one of six different emotions, generated by 12 Text-to-Image (T2I) models. Unlike previous works, MEMO-Bench provides a framework for evaluating both T2I models and MLLMs in the context of sentiment analysis. Additionally, a progressive evaluation approach is employed, moving from coarse-grained to fine-grained metrics, to offer a more detailed and comprehensive assessment of the sentiment analysis capabilities of MLLMs. The experimental results demonstrate that existing T2I models are more effective at generating positive emotions than negative ones. Meanwhile, although MLLMs show a certain degree of effectiveness in distinguishing and recognizing human emotions, they fall short of human-level accuracy, particularly in fine-grained emotion analysis. The MEMO-Bench will be made publicly available to support further research in this area.

Related papers

• 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)
• Contextual Emotion Recognition using Large Vision Language Models [0.6749750044497732]
  Achieving human-level recognition of the apparent emotion of a person in real world situations remains an unsolved task in computer vision. In this paper, we examine two major approaches enabled by recent large vision language models. We demonstrate that a vision language model, fine-tuned even on a small dataset, can significantly outperform traditional baselines.
  arXiv  Detail & Related papers  (2024-05-14T23:24:12Z)
• Fine-grained Affective Processing Capabilities Emerging from Large Language Models [7.17010996725842]
  We explore ChatGPT's zero-shot ability to perform affective computing tasks using prompting alone. We show that ChatGPT a) performs meaningful sentiment analysis in the Valence, Arousal and Dominance dimensions, b) has meaningful emotion representations in terms of emotion categories, and c) can perform basic appraisal-based emotion elicitation of situations.
  arXiv  Detail & Related papers  (2023-09-04T15:32:47Z)
• HICEM: A High-Coverage Emotion Model for Artificial Emotional Intelligence [9.153146173929935]
  Next-generation artificial emotional intelligence (AEI) is taking center stage to address users' desire for deeper, more meaningful human-machine interaction. Unlike theory of emotion, which has been the historical focus in psychology, emotion models are a descriptive tools. This work has broad implications in social robotics, human-machine interaction, mental healthcare, and computational psychology.
  arXiv  Detail & Related papers  (2022-06-15T15:21:30Z)
• 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)
• Emotion Recognition from Multiple Modalities: Fundamentals and Methodologies [106.62835060095532]
  We discuss several key aspects of multi-modal emotion recognition (MER) We begin with a brief introduction on widely used emotion representation models and affective modalities. We then summarize existing emotion annotation strategies and corresponding computational tasks. Finally, we outline several real-world applications and discuss some future directions.
  arXiv  Detail & Related papers  (2021-08-18T21:55:20Z)
• Enhancing Cognitive Models of Emotions with Representation Learning [58.2386408470585]
  We present a novel deep learning-based framework to generate embedding representations of fine-grained emotions. Our framework integrates a contextualized embedding encoder with a multi-head probing model. Our model is evaluated on the Empathetic Dialogue dataset and shows the state-of-the-art result for classifying 32 emotions.
  arXiv  Detail & Related papers  (2021-04-20T16:55:15Z)
• Multitask Learning for Emotion and Personality Detection [17.029426018676997]
  We build on the known correlation between personality traits and emotional behaviors, and propose a novel multitask learning framework, SoGMTL. Our more computationally efficient CNN-based multitask model achieves the state-of-the-art performance across multiple famous personality and emotion datasets.
  arXiv  Detail & Related papers  (2021-01-07T03:09:55Z)
• 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)

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.