Contains 145k captions for 28k images. The dataset challenges a model to recognize text, relate it to its visual context, and decide what part of the text to copy or paraphrase, requiring spatial, semantic, and visual reasoning between multiple text toke
Our dataset challenges a model to recognize text, relate it to its visual context, and decide what part of the text to copy or paraphrase, requiring spatial, semantic, and visual reasoning between multiple text tokens and visual entities, such as objects. We study baselines and adapt existing ...
We provide an example script for training on TextCaps dataset for 12000 iterations and evaluating every 500 iterations. ./train.sh This may take approximately 13 hours, depending on GPU devices. Please refer to our paper for implementation details. ...
The CapsNet performance drastically improves when training afresh with the newly generated dataset. The following figure illustrates the performaces of CapsNets trained with the original dataset, as well as the generated dataset with only 0.5% additional data, generated with our system. ...
Our model outperforms state-of-the-art models on TextCaps dataset, improving from 81.0 to 93.0 in CIDEr. Our source code is publicly available. 展开 DOI: 10.48550/arXiv.2012.03662 年份: 2020 收藏 引用 批量引用 报错 分享 全部来源 免费下载 求助全文 arXiv.org ResearchGate 钛学术 钛学术 (全网...
TextCaps: A Dataset for Image Captioning with Reading Comprehension,程序员大本营,技术文章内容聚合第一站。
Our method outperforms state-of-the-art models on the TextCaps dataset, improving from 105.0 to 107.2 in CIDEr.doi:10.1007/978-3-031-15919-0_62Qiang LiBing LiCan MaSpringer, Cham