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
仅用200个样本就能得到当前最佳结果:手写字符识别新模型TextCaps 由于深度学习近期取得的进展,手写字符识别任务对一些主流语言来说已然不是什么难题了。但是对于一些训练样本较少的非主流语言来说,这仍是一个挑战性问题。为此,本文提出新模型TextCaps,它每类仅用200个训练样本就能达到和当前最佳水平媲美的结果。 由于深度...
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 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