  We propose a method for automatically answering questions about images by
bringing together recent advances from natural language processing and computer
vision. We combine discrete reasoning with uncertain predictions by a
multi-world approach that represents uncertainty about the perceived world in a
bayesian framework. Our approach can handle human questions of high complexity
about realistic scenes and replies with range of answer like counts, object
classes, instances and lists of them. The system is directly trained from
question-answer pairs. We establish a first benchmark for this task that can be
seen as a modern attempt at a visual turing test.
