VideoNavQA Dataset

The VideoNavQA dataset contains pairs of questions and videos generated in the House3D environment. The goal of this dataset is to assess question-answering performance from nearly-ideal navigation paths, while considering a much more complete variety of questions than current instantiations of the Embodied Question Answering (EQA) task.

VideoNavQA contains approximately 101,000 pairs of videos and questions, 28 types of questions belonging to 8 categories, with 70 possible answers. Each question type is
associated with a template that facilitates programmatic generation using ground truth information extracted from the video. The complexity of the questions in the dataset is far beyond that of other similar tasks using this generation method (such as CLEVR): the questions involve single or multiple object/room existence, object/room counting, object color recognition and localization, spatial reasoning, object/room size comparison and equality of object attributes (color, room location).

VideoNavQA数据集包含在House3D环境中生成的成对问题与视频。本数据集旨在评估从近乎理想的导航路径中进行的问答性能，同时考虑比目前具身问答（EQA）任务实例更为丰富的各类问题。VideoNavQA数据集包含大约101,000对视频与问题，涵盖8个类别中的28种问题类型，并设有70个可能的答案。每种问题类型都与一个模板相关联，该模板利用从视频中提取的真相信息，便于进行程序化的生成。数据集中问题的复杂性远超采用类似生成方法的其它类似任务（如CLEVR）：问题涉及单个或多个物体/房间的存在、计数、物体颜色识别与定位、空间推理、物体/房间大小比较以及物体属性（颜色、房间位置）的相等性。