Affection: Learning Affective Explanations for Real-World Visual Data

• URL: http://arxiv.org/abs/2210.01946v1
• Date: Tue, 4 Oct 2022 22:44:17 GMT
• Title: Affection: Learning Affective Explanations for Real-World Visual Data
• Authors: Panos Achlioptas, Maks Ovsjanikov, Leonidas Guibas and Sergey Tulyakov
• Abstract summary: We introduce and share with the research community a large-scale dataset that contains emotional reactions and free-form textual explanations for 85,007 publicly available images. We show that there is significant common ground to capture potentially plausible emotional responses with a large support in the subject population. Our work paves the way for richer, more human-centric, and emotionally-aware image analysis systems.
• Score: 50.28825017427716
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this work, we explore the emotional reactions that real-world images tend to induce by using natural language as the medium to express the rationale behind an affective response to a given visual stimulus. To embark on this journey, we introduce and share with the research community a large-scale dataset that contains emotional reactions and free-form textual explanations for 85,007 publicly available images, analyzed by 6,283 annotators who were asked to indicate and explain how and why they felt in a particular way when observing a specific image, producing a total of 526,749 responses. Even though emotional reactions are subjective and sensitive to context (personal mood, social status, past experiences) - we show that there is significant common ground to capture potentially plausible emotional responses with a large support in the subject population. In light of this crucial observation, we ask the following questions: i) Can we develop multi-modal neural networks that provide reasonable affective responses to real-world visual data, explained with language? ii) Can we steer such methods towards producing explanations with varying degrees of pragmatic language or justifying different emotional reactions while adapting to the underlying visual stimulus? Finally, iii) How can we evaluate the performance of such methods for this novel task? With this work, we take the first steps in addressing all of these questions, thus paving the way for richer, more human-centric, and emotionally-aware image analysis systems. Our introduced dataset and all developed methods are available on https://affective-explanations.org

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