AI Challenger : A Large-scale Dataset for Going Deeper in Image Understanding
This provides a new dataset for researchers in computer vision to evaluate and improve methods for tasks like keypoint detection and captioning, but it is incremental as it builds on existing dataset efforts.
The paper tackles the limitation of large-scale datasets for complex computer vision tasks beyond classification by introducing the AI Challenger (AIC) dataset, which includes three sub-datasets for human keypoint detection, attribute recognition, and image captioning with rich annotations, serving as a benchmark and pre-training resource.
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.