Automatic Annotation of Structured Facts in Images
This addresses the need for efficient data collection for fact-level image understanding, though it is incremental as it builds on existing caption-based methods.
The paper tackled the problem of automatically annotating structured visual facts from images with captions, achieving 83% accuracy and collecting over 380,000 annotations in less than a day.
Motivated by the application of fact-level image understanding, we present an automatic method for data collection of structured visual facts from images with captions. Example structured facts include attributed objects (e.g., <flower, red>), actions (e.g., <baby, smile>), interactions (e.g., <man, walking, dog>), and positional information (e.g., <vase, on, table>). The collected annotations are in the form of fact-image pairs (e.g.,<man, walking, dog> and an image region containing this fact). With a language approach, the proposed method is able to collect hundreds of thousands of visual fact annotations with accuracy of 83% according to human judgment. Our method automatically collected more than 380,000 visual fact annotations and more than 110,000 unique visual facts from images with captions and localized them in images in less than one day of processing time on standard CPU platforms.