In this paper, we design and train a Generative Image-to-text Transformer, GIT, to unify vision-language tasks such as image/video captioning and question answering. While generative models provide a consistent network architecture between pre-training and fine-tuning, existing work typically contains complex structures (uni/multi-modal encoder/decoder) and depends on external modules such as object detectors/taggers and optical character recognition (OCR). In GIT, we simplify the architecture as one image encoder and one text decoder under a single language modeling task. We also scale up the pre-training data and the model size to boost the model performance. Without bells and whistles, our GIT establishes new state of the arts on 12 challenging benchmarks with a large margin. For instance, our model surpasses the human performance for the first time on TextCaps (138.2 vs. 125.5 in CIDEr). Furthermore, we present a new scheme of generation-based image classification and scene text recognition, achieving decent performance on standard benchmarks.

本文设计和训练了一个生成式图像到文本的转换器——GIT，以统一图像/视频字幕和问答等视觉语言任务。使用简化的体系结构和扩大的预训练数据和模型规模，GIT在12个具有挑战性的基准测试中都取得了新的最佳表现，这些基准测试中有TextCaps、图像分类和场景文本识别等。

GIT: 用于视觉和语言的生成图像到文本的变压器