Visual Question Answering (VQA) has recently appeared as a hot research area in the field of computer vision and natural language processing. A VQA model uses both image and question features and fuses them to predict an answer for a given natural question related to an image. However, most ...
我们研究了对Kazemi和Elqursh提出的最先进的VQA模型(Show, ask, attend, and answer: A strong baseline for visual question answering.)的攻击,并在VQA数据集上证明了我们的方法的有效性。 1、简介 在这个越来越多的人试图通过人工构建与现实数据样本非常相似但破坏模型正确执行能力的对抗性样本来打破深网模型神圣...
model.add(Convolution2D(512, 3, 3, activation='relu')) model.add(MaxPooling2D((2,2), strides=(2,2))) model.add(ZeroPadding2D((1,1))) model.add(Convolution2D(512, 3, 3, activation='relu')) model.add(ZeroPadding2D((1,1))) model.add(Convolution2D(512, 3, 3, activation='relu...
huggingface多模态模型visual-question-answering详解-回复 Huggingface是一家自然语言处理(NLP)技术的领先者,他们提供了许多强大的工具和模型,来帮助开发者构建和部署NLP应用。其中,多模态模型是Huggingface提供的一项重要功能,可用于处理同时包含文本和图像的数据。本文将详细介绍Huggingface多模态模型中的一个重要任务——...
视觉问答(Visual Question Answering,VQA),是一种涉及计算机视觉和自然语言处理的学习任务。这一任务的定义如下: A VQA system takes as input an image and a free-form, open-ended, natural-language question about the image and produces a natural-language answer as the output[1]。 翻译为中文:一个VQA...
model.add(Dropout(0.5)) model.add(Dense(1000, activation='softmax'))ifweights_path: model.load_weights(weights_path)returnmodel 2.2.2 处理输⼊源数据:⽂字 2.3 第三步, 选取VQA模型-MLP 2.3.1 选取VQA模型-MLP 2.3.2 选取VQA模型-LSTM...
visual question answering (VQA) is a learning task involving two major fields of computer vision and natural language processing. The development of deep learning technology has contributed to the advancement of this research area. Although the research on the question answering model has made great ...
2.1 Bottom-Up Attention Model 空间图像特征V的定义是通用的。在这项工作中,作者根据边界框定义空间区域,并使用Faster R-CNN实现自下而上的注意力。Faster R-CNN是一个对象检测模型,旨在识别属于特定类别的对象实例,并使用边界框对其进行定位。Faster R-CNN通过卷积层提取输入图像的特征图,将特征图送入RPN(Region...
3. Proposed Model: 作者将 VQA 系统分为两个部分:第一个部分就是感知,the embedding part that encodes the input question and image;第二个部分就是,the classifier part that handles the reasoning and actural question answering; 3.1. 非线性映射ftheta(∗)ftheta(∗): ...
Visual question answering: Datasets, algorithms, and future challenges- Kushal Kafle et al,CVIU 2017. Visual question answering: A survey of methods and datasets- Qi Wu et al,CVIU 2017. 2019 Combining Multiple Cues for Visual Madlibs Question Answering- Tatiana Tommasi et al,IJCV 2019. [code]...