使用CNN提取图像特征,使用LSTM作为解码器生成对应的图像描述. 二、transformer 1、BLIP 论文:BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation 链接:https://arxiv.org/abs/2201.12086 源码:https://github.com/salesforce/BLIP 作者分析了已有的模型在模型结构...
可以通过多种方式将CNN的输出与下个RNN相连,但是在所有的方式中,从CNN中提取的特征向量都需要经历一些处理步骤才能用作RNN第一个单元的输入。有时候,在将CNN输出用作RNN的输入之前,使用额外的全连接层或线性层解析CNN输出。 这与迁移学习很相似,使用过的CNN经过预先训练,在其末尾添加一个未训练过的线性层使我们能...
Furthermore, a generative merge model based on Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) is applied especially for Myanmar image captioning. Next, two conventional feature extraction models Visual Geometry Group (VGG) OxfordNet 16-layer and 19-layer are compared. The ...
"Show and Tell", simple LSTM RNN:Vinyals, Oriol, et al. "Show and tell: Lessons learned from the 2015 MSCOCO image captioning challenge." "Show, Attend and Tell", LSTM RNN with attention:Xu, K., et al. "Show, attend and tell: Neural image caption generation with visual attention."...
from the image, and uses a LSTM recurrent neural network to decode these features into a sentence. A soft attention mechanism is incorporated to improve the quality of the caption. This project is implemented using the Tensorflow library, and allows end-to-end training of both CNN and RNN ...
在neuraltalk2中,LSTM层的输入(Embedding层的输出)向量维度和LSTM隐藏层的向量维度均设置为512。zsdonghao/Image-Captioning的设置相同。 在zsdonghao/Image-Captioning中,作者将vocabulary_size设置为12000。 版权声明:本文为博主原创文章,欢迎转载,转载请注明作者及原文出处!
我们将所有输入作为序列传递给LSTM,序列如下所示:1.首先从图像中提取特征向量;2. 然后是一个单词,下一个单词等。 嵌入维度(Embedding Dimention) 当LSTM按顺序查看输入时,序列中的每个输入需要具有一致的大小,因此嵌入特征向量和每个单词它们都是embed_size ...
Image Captioning with Semantic Attention(CVPR 2016) (Related work)divided Image Caption into two categories:top-down and bottom-up Bottom-up: the classical ones(templated-based), start with visual concepts, objects, attributes, words and phrases, and combine them into sentences using language models...
VL-BERT: Pre-training of Generic Visual-Linguistic Representations, ICLR 2020[code] 与上述两个模型相同,VL-BERT 在结构上依旧直接采用堆叠的 Transformer。如下图所示其在输入端与上述两个模型略有不同。最主要区别还在于前面两篇文章中的Faster RCNN是pre-train好的直接用于提取图片区域特征,而这篇文章的Faste...
variation in the image to locate an extra component for correlation, and then built up a CNN for getting the results19, still has low accuracy. An innovative technique that does not need a pre-trained model for executing the system was created using a capsule network and versatile pooling11....