3.2 Interleaved Visual Language Corpus Helps Pre-training (交错视觉语言语料库有助于预训练); 超大文本图片交织数据集开源 -- MMC4 图像/文档交织, 这块目前考虑先用coda-llm, 更符合目标场景. 它包含了图像和文本的交错序列。这种交错格式不仅支持通过交错独立的监督样本(图像、文本)进行少样本学习,而且还支持...
TL;DR: The paper explores different design options for pre-training visual language models (VLMs). The main findings are: Updating/fine-tuning the language model (LLM) backbone during pre-training is important for aligning the visual and textual embeddings and enabling in-context learning capabilit...
Visual language models (VLMs) rapidly progressed with the recent success of large language models. There have been growing efforts on visual instruction tuning to extend the LLM with visual inputs, but lacks an in-depth study of the visual language pre-training process, where the model learns ...
In the past few years, the emergence of pre-training models has brought uni-modal fields such as computer vision (CV) and natural language processing (NLP)
pre-trainingtransformersmultimodal learningrepresentation learningIn the past few years,the emergence of pre-training models has brought uni-modal fields such as computer vision(CV)and natural language processing(NLP)to a new era.Substantial works have shown that they are beneficial for downstream uni-...
Visual perception based multi-modal pre-trained models Image and video synthesis/generation based on multi-modal pre-trained models Vision-language understanding Multi-modality fusion Open-set problems for multi-modality understanding ...
Vision-Language Pre-training (VLP) models have achieved remarkable success in practice, while easily being misled by adversarial attack. Though harmful, adversarial attacks are valuable in revealing the blind-spots of VLP models and promoting their robustness. However, existing adversarial attacking studi...
Image-Text VLP models VisualBERT[Li et al.,2019]被称为第一个图像-文本预训练模型,它使用由更快的R-CNN提取的视觉特征,将视觉特征和文本嵌入连接起来,然后将连接起来的特征馈送给由BERT初始化的单个转换器。许多VLP模型[Li et al.,2020a;Su et al.,2019;Chen et al.,2020;Qi et al.,2020]在调整训...
对海量的用户文本评论数据进行准确分类具有重要的经济效益和社会效益.目前大部分文本分类方法是将文本编码直接使用于各式的分类器之前,而忽略了标签文本中蕴含的提示信息.针对以上问题,提出一种基于RoBERTa(Robustly optimized BERT pretraining approach)的文本和标签信息融合分类模型(TLIFC-RoBERTa).首先,利用RoBERTa预训练...
(FP16) pretraining. This not only significantly improves the efficiency of transformer training and inference by 20%, but also provides better numerical stability in mixed-precision training. The latter is one of the most important needs when ...