Affection: Learning Affective Explanations for Real-World Visual Data

TL;DR

This paper introduces a large dataset and neural models to generate and explain emotional reactions to real-world images using natural language, advancing emotionally-aware image analysis.

Contribution

The work provides the first large-scale dataset of emotional responses with textual explanations and develops neural methods to generate and steer affective explanations for images.

Findings

Significant common ground in emotional responses across individuals.

Neural models can produce plausible affective explanations.

Methods can be steered to vary explanations and emotional justifications.

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