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Auto Alt Text

Auto Alt Text

用户数 : 2000 分类 : 无障碍 扩展大小 : 36.88KiB 最后更新时间 : 2020-09-09 版本 : 1.0.0.3
  • Auto Alt Text
                                                            

Auto Alt Text 的使用方法详解,最全面的教程

Auto Alt Text 描述:

用户数:2000 分类:无障碍 扩展大小:36.88KiB 最后更新时间:2020-09-09 版本:1.0.0.3

Auto Alt Text 插件简介:

这是来自Chrome商店的 Auto Alt Text 浏览器插件,您可以在当前页面下载它的最新版本安装文件,并安装在Chrome、Edge等浏览器上。

Auto Alt Text 插件下载方法/流程:

点击下载按钮,关注“扩展迷Extfans”公众号并获取验证码,在网页弹窗中输入验证码,即可下载最新安装文件。

Auto Alt Text 插件安装教程/方法:

(1)将扩展迷上下载的安装包文件(.zip)解压为文件夹,其中类型为“crx”的文件就是接下来需要用到的安装文件 (2) 从设置->更多工具->扩展程序 打开扩展程序页面,或者地址栏输入 Chrome://extensions/ 按下回车打开扩展程序页面 (3) 打开扩展程序页面的“开发者模式” (4) 将crx文件拖拽到扩展程序页面, 完成安装如有其它安装问题, 请扫描网站底部二维码与客服联系如有疑问请参考:https://www.extfans.com/installation/
Use the power of AI to caption images with a simple right-click. Let's make the web a more accessible place. What is this? Auto Alt Text is a chrome extension that can generate descriptive captions for pictures. Currently, users who are visually impaired must rely on metadata and alt-text descriptions put in by website developers in order to understand what an image actually contains. However, not all web developers take the time to caption all their images. This is where Auto Alt Text steps in. Using artificial intelligence, the extension can analyze an image and detect the contents of the scene depicted in it within 5 seconds! How does it work? It's pretty simple to get up and running!: Download the extension Right click on any image element (note does not work with background images at the moment) Click "Get Image Info" from the dropdown Wait a few seconds and get your caption What is the tech behind it? Auto Alt Text is based off of the im2txt model which was created by Vinyals et al for the 2015 MCOCO Image Captioning Challenge. The model itself is based off of a encoder-decoder neural network (basically a deep conv net paired with a LSTM). The deep conv net first encodes an image into a vector representation using Inception v3 (a popular image recognition model). The LSTM then creates a captioning model based on the Inception v3 encodings. I converted the model into an API and pared it down so that it could fit on a Lambda instance and stay loaded into memory for blazing fast responses under 5 seconds (compared to the > 15 seconds needed for the model to classify out of the box).
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