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...
摘要原文 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...
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)
The zero-shot classification performance of large-scale vision-language pre-training models (e.g., CLIP, BLIP and ALIGN) can be enhanced by incorporating a prompt (e.g., "a photo of a [CLASS]") before the class words. Modifying the prompt slightly can have significant effect on the clas...
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 ...
VLP: A Survey on Vision-Language Pre-training VLP:视觉语言预训练研究综述 论文地址: https://arxiv.org/pdf/2202.09061.pdf 摘要: 在过去几年中,训练前模型的出现将计算机视觉(CV)和自然语言处理(NLP)等单峰领域带入了一个新时代。大量工作表明,它们有利于下游单峰任务,避免从头开始训练新模型。那么,这种预先...
T-NLRv5 is largely based on our recent work,COCO-LM(opens in new tab), a natural evolution of pretraining paradigm converging the benefits of ELECTRA-style models and corrective language model pretraining. As illustrated in Figure 2, T-NLR...
While multi-modal foundation models pre-trained on large-scale data have been successful in natural language understanding and vision recognition, their use in medical domains is still limited due to the fine-grained nature of medical tasks and the high demand for domain knowledge. To address this...
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. NAACL 2019. [pdf] [code & model] Language Models are Unsupervised Multitask Learners. Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amode...