Abstract
Visual Question Answering (VQA) is a novel problem domain where multi-modal inputs must be processed in order to solve the task given in the form of a natural language. As the solutions inherently require to combine visual and natural language processing with abstract reasoning, the problem is considered as AI-complete. Recent advances indicate that using high-level, abstract facts extracted from the inputs might facilitate reasoning. Following that direction we decided to develop a solution combining state-of-the-art object detection and reasoning modules. The results, achieved on the well-balanced CLEVR dataset, confirm the promises and show significant, few percent improvements of accuracy on the complex "counting" task.
Abstract (translated)
视觉问答（VQA）是一个新的问题领域，其中多模式输入必须被处理以解决以自然语言形式给出的任务。由于解决方案固有地需要将视觉和自然语言处理与抽象推理相结合，因此该问题被视为AI完整。最近的进展表明，使用从输入中提取的高级抽象事实可能有助于推理。遵循这个方向，我们决定开发一个结合了最先进的物体检测和推理模块的解决方案。在均衡的CLEVR数据集上取得的结果证实了这一承诺，并且显示出对复杂“计数”任务的准确度有显着提高的几个百分点。
URL
https://arxiv.org/abs/1801.09718
