Microsoft COCO Captions: Data Collection and Evaluation Server
Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick
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ReproduceCode
- github.com/tylin/coco-captionOfficialIn papernone★ 0
- github.com/ruotianluo/coco-captionpytorch★ 67
- github.com/jmhessel/pycocoevalcappytorch★ 14
- github.com/peteanderson80/coco-captionnone★ 0
- github.com/lvapeab/coco-captionnone★ 0
- github.com/luoweizhou/coco-captionnone★ 0
- github.com/NUS-VIP/salicon-evaluationnone★ 0
- github.com/qingzwang/DiversityMetricstf★ 0
- github.com/wenhuchen/data-to-text-evaluation-metricnone★ 0
- github.com/salaniz/pycocoevalcapnone★ 0
Abstract
In this paper we describe the Microsoft COCO Caption dataset and evaluation server. When completed, the dataset will contain over one and a half million captions describing over 330,000 images. For the training and validation images, five independent human generated captions will be provided. To ensure consistency in evaluation of automatic caption generation algorithms, an evaluation server is used. The evaluation server receives candidate captions and scores them using several popular metrics, including BLEU, METEOR, ROUGE and CIDEr. Instructions for using the evaluation server are provided.