论文笔记7:Knowledge-enhanced visual-language pre-training on chest radiology images juezhi 创作声明:包含 AI 辅助创作 1 人赞同了该文章 目录 收起 1 概览 2 Introduction 3 Related Work 4 Method 4.1 算法概述 4.2 问题场景 4.3 Knowledge encoder 4.4 Entity extraction 4.5 Knowledge-guided visual...
The methods adopting the powerful BERT model as the backbone for learning joint representation of image content and natural language have shown promising improvements on VCR. However, none of the existing methods have utilized commonsense knowledge in visual commonsense reasoning, which we believe will ...
In this paper, we consider the problem of enhancing self-supervised visual-language pre-training (VLP) with medical-specific knowledge, by exploiting the paired image-text reports from the radiological daily practice. In particular, we make the following contributions 在本文中,我们考虑了增强自我监督...
一个突出的模型是VisualBERT [48][Visualbert: A simple and performant baseline for vision and language],它倾向于将BERT架构纳入视觉信息。它利用大规模图像-文本数据集的预训练来共同学习视觉和文本模态的表示。VisualBERT通过利用大规模图像标题数据来学习对齐图像和文本。它使用屏蔽的令牌预测任务,其中视觉和文本...
Learning transferable visual models from natural language supervision. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th Interna- tional Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, volume 139 of Proceedings ...
Lifelong topic modeling has attracted much attention in natural language processing (NLP), since it can accumulate knowledge learned from past for the future task. However, the existing lifelong topic models often require complex derivation or only utilize part of the context information. In this stu...
we first retrieve facts for the navigation views from the constructed knowledge base. And than we build a knowledge enhanced reasoning network, containing purification, fact-aware interaction, and instruction-guided aggregation modules, to integrate the visual features, history features, instruction feature...
people were still limited to the paradigm of supervised learning and thought without enough labeled data it would be difficult to unleash the potential of deep learning. However, with the emergence of self-supervised learning, big language models such as BERT [3] can learn a lot of knowledge ...
Building on IFShip, we develop an FGSC visual chatbot that redefines the FGSC problem as a step-by-step reasoning task and conveys the reasoning process in natural language. Experimental results reveal that the proposed method surpasses state-of-the-art FGSC algorithms in both classification ...
Cognitive Visual-Language Mapper (CVLM), which contains a pretrained Visual Knowledge Aligner (VKA) and a Fine-grained Knowledge Adapter (FKA) used in the multimodal instruction tuning stage. Specifically, we design the VKA based on the interaction between a small language model and a visual ...