  Significant progress has been achieved in Computer Vision by leveraging
large-scale image datasets. However, large-scale datasets for complex Computer
Vision tasks beyond classification are still limited. This paper proposed a
large-scale dataset named AIC (AI Challenger) with three sub-datasets, human
keypoint detection (HKD), large-scale attribute dataset (LAD) and image Chinese
captioning (ICC). In this dataset, we annotate class labels (LAD), keypoint
coordinate (HKD), bounding box (HKD and LAD), attribute (LAD) and caption
(ICC). These rich annotations bridge the semantic gap between low-level images
and high-level concepts. The proposed dataset is an effective benchmark to
evaluate and improve different computational methods. In addition, for related
tasks, others can also use our dataset as a new resource to pre-train their
models.
