Personality-aware Human-centric Multimodal Reasoning: A New Task, Dataset and Baselines
This paper presents a new approach to understanding how personality traits influence people's behavior in social situations by analyzing various types of information from videos.
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- 1 Existing studies primarily concentrated on objective information related to individuals, while neglecting the incorporation of individual personality traits in reasoning.
- 2 On the other hand,recently a category of human-centric multimodal reasoning tasks, including Social-IQ , VLEP , and PMR , has emerged with the aim to infer individuals' psychological states and behaviors by leveraging the available multimodal information.
- 3 We employ a pre-trained multimodal model to extract multimodal features and incorporate personality traits, to predict the most possible behavior.
- 4 In our preliminary experiment, it was observed that when the options are presented in the form of QA pairs, the model tends to over emphasize the importance of questions.
Introduction
Personality encapsulates an individual’s psychological characteristics and behavioral tendencies. However, on the one hand, in the field of affective computing, there is often a focus on predicting and analyzing personality traits, without applying them to the prediction of future events related to individuals.
This is achieved by integrating multimodal signals from past moments and considering their personality traits.
We acquire the personality of the main characters through the PDB foot_1 website, and utilize them as the personality annotations in our dataset.
Research Question
Existing studies primarily concentrated on objective information related to individuals, while neglecting the incorporation of individual personality traits in reasoning.
Methodology
In this work, we introduce a new task called Personality-aware Human-centric Multimodal Reasoning (PHMR) (T foot_0 ). The goal of our task is to forecast the most probable behavior of a particular individual in future scenarios within intricate social interactions that encompass multiple individuals and long-term interactions.
Study Design
To support this task, we construct a dataset based on six television shows from TVQA , which we refer to as the Personalityaware Human-centric Multimodal Reasoning Dataset (PHMRD).
Following the mainstream settings of multimodal reasoning tasks, we also define our task as a multiple choice problem.
Due to space limitation, Our focus in this work is on the presentation of the task and the dataset.
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Results & Findings
An individual’s personality traits, emotions, and beliefs are essential components of their individual differences, and they shape their behavioral choices and decision-making processes in different situations. The reactions of individuals, character-ized by diverse personality traits, to the identical situations can exhibit significant variations.
- An individual’s personality traits, emotions, and beliefs are essential components of their individual differences, and they shape their behavioral choices and decision-making processes in different situations.
- The reactions of individuals, character-ized by diverse personality traits, to the identical situations can exhibit significant variations.
- On the other hand,recently a category of human-centric multimodal reasoning tasks, including Social-IQ , VLEP , and PMR , has emerged with the aim to infer.
- We employ a pre-trained multimodal model to extract multimodal features and incorporate personality traits, to predict the most possible behavior.
- The experimental results reveal that the incorporation of personality traits can enhance reasoning performance in both unimodal and multimodal settings.
Given the limited number of available episodes for each TV show, training separate models individually proves to be a challenge.
On the other hand,recently a category of human-centric multimodal reasoning tasks, including Social-IQ , VLEP , and PMR , has emerged with the aim to infer individuals’ psychological states and behaviors by leveraging the available multimodal information.
Practical Applications
For example, when faced with an event of “Person 1 sing a song in public for Person 0 “, a cautious and sensitive Person 0 may feel embarrassed, whereas a confident and lively Person 0 may feel excited. For example, it should be inferred that “Person 0 felt gratitude” is the most possible option.
It is now common knowledge that our personalities are our own, that they filter and interpret what we see and hear, and that the basic principle of Type 9 personality theory is that each of us has one of nine possible “filters” that will keep the blueprint for our lives and the general It is.
Task
The primary task, Personality-aware Human-centric Multimodal Reasoning (PHMR), focuses on predicting individual behavior in long-term interactions influenced by personality traits. It utilizes multimodal information including video, audio, dialogue, and personality annotations to forecast behavior.
Dataset
A new dataset, the Personality-aware Human-centric Multimodal Reasoning Dataset (PHMRD), is constructed from six television shows. It includes statistics on samples, video duration, dialogue count, and personality annotations, providing a comprehensive resource for the PHMR task.
Frequently Asked Questions
However, on the one hand, in the field of affective computing, there is often a focus on predicting and analyzing personality traits, without applying them to the prediction of future events related to individuals. Existing studies primarily concentrated on objective information related.
The goal of our task is to forecast the most probable behavior of a particular individual in future scenarios within intricate social interactions that encompass multiple individuals and long-term interactions. To support this task, we construct a dataset based on six television.
On the other hand,recently a category of human-centric multimodal reasoning tasks, including Social-IQ , VLEP , and PMR , has emerged with the aim to infer individuals’ psychological states and behaviors by leveraging the available multimodal information. We employ a pre-trained multimodal.
For example, it should be inferred that “Person 0 felt gratitude” is the most possible option. Therefore, we opt for a trained model on the complete dataset and evaluate its performance on the test sets of these six television shows.
Given the limited number of available episodes for each TV show, training separate models individually proves to be a challenge. Due to space limitation, Our focus in this work is on the presentation of the task and the dataset.
This paper presents a new approach to understanding how personality traits influence people’s behavior in social situations by analyzing various types of information from videos.