Combining Knowledge and Reasoning through Probabilistic Soft Logic for Image Puzzle Solving

The uncertainty associated with human perception is often reduced by one's extensive prior experience and knowledge. Current datasets and systems do not emphasize the necessity and benefit of using such knowledge. In this work, we propose the task of solving a genre of image-puzzles ("image riddles") that require both capabilities involving visual detection (including object, activity recognition) and, knowledge-based or commonsense reasoning. Each puzzle involves a set of images and the question "what word connects these images?". We compile a dataset of over 3k riddles where each riddle consists of 4 images and a groundtruth answer. The annotations are validated using crowd-sourced evaluation. We also define an automatic evaluation metric to track future progress. Our task bears similarity with the commonly known IQ tasks such as analogy solving, sequence filling that are often used to test intelligence. We develop a Probabilistic Reasoning-based approach that utilizes commonsense knowledge about words and phrases to answer these riddles with a reasonable accuracy. Our approach achieves some promising results for these riddles and provides a strong baseline for future attempts.