image caption的目标就是根据提供的图像,输出对应的文字描述。 对于图片描述任务,应该尽可能写实,即不需要华丽的语句,只需要陈述图片所展现的事实即可。根据常识,可以知道该任务一般分为两个部分,一是图片编码,二是文本生成,基于此后续的模型也都是encoder-decoder的结构。 人类可以将图像中的视觉信息自动建立关系,进而...
Image caption generation using transformer learning methods: a case study on instagram imageImage CaptioningTransformer Learning ModelSelf-Attention MechanismEncoder-DecoderImage feature extractionInstagram imageNowadays, images are being used more extensively for communication purposes. Asingle image can convey ...
与“CNN+Transformer”的设计范式相比,该模型可以从一开始就在每个编码器层上建模全局上下文,并且完全没有卷积。 FrameWork Encoder 如上图所示,作者没有像之前的方法那样使用预先训练的CNN或Faster R-CNN模型来提取空间特征或自下而上的特征,而是选择对输入图像进行排序,并将图像标注作为序列到序列预测任务。具体地说,...
auto=compress&cs=tinysrgb&w=600" # Display Image display(load_image(url)) # Display Caption ...
模型特点 ViT模型是应用于图像分类领域。因此,其模型结构相较于传统的Transformer有以下几个特点:数据集...
I am trying to produce a model that will produce a caption for an image using resnet as the encoder, transformer as the decoder and COCO as the database.After training my model for 10 epochs, my model failed to produce anything other than the word <pad> which implies that ...
但是由于 caption 未必能覆盖图片的全部信息,因此这一方法存在性能瓶颈。另一个容易想到的解决方案是,在预训练的 LLM 基础上,增加用来对接另一个模态输入的网络参数,并通过微调来得到一个跨模态的大模型。 Deepmind 的 Flamingo [3] 模型采用了这一方案,训练了一个 800 万参数量的视觉-语言模型,并在 OK-VQA ...
PyTorch training code and pretrained models forCATR(CAptionTRansformer). The models are also available via torch hub, to load model with pretrained weights simply do: model=torch.hub.load('saahiluppal/catr','v3',pretrained=True)# you can choose between v1, v2 and v3 ...
As the experiment going, we found that our transformer model don't have a good performance inevalmodule. However, the caption result seems to be grammatical correctly and expressional accurately in our local PC. After debugging, we found that the same model with the same input would generate ...
对描述编码 encode_caption 对描述解码 decode_caption 描述正确性指标 预处理flickr图片(resize.py) 构建经典的CNN网络 使用构建的网络来预训练图片(迁移学习) 获得和保存图片对应的特征(vgg-16为例是4096维的特征) 构建ImageCaptioning模型(train.py) - NIC: CNN编码+LSTM解码网络结构 ...