Intentonomy: a Dataset and Study towards Human Intent Understanding
This work addresses the challenge of interpreting human motives from images for applications in social media analysis, though it is incremental as it builds on existing visual recognition methods.
The authors tackled the problem of understanding human intent from social media images by introducing Intentonomy, a dataset of 14K images annotated with 28 intent categories, and found that visual and textual information significantly impact intent prediction, with specific contributions quantified in their study.
An image is worth a thousand words, conveying information that goes beyond the physical visual content therein. In this paper, we study the intent behind social media images with an aim to analyze how visual information can help the recognition of human intent. Towards this goal, we introduce an intent dataset, Intentonomy, comprising 14K images covering a wide range of everyday scenes. These images are manually annotated with 28 intent categories that are derived from a social psychology taxonomy. We then systematically study whether, and to what extent, commonly used visual information, i.e., object and context, contribute to human motive understanding. Based on our findings, we conduct further study to quantify the effect of attending to object and context classes as well as textual information in the form of hashtags when training an intent classifier. Our results quantitatively and qualitatively shed light on how visual and textual information can produce observable effects when predicting intent.