AI tool detects global fashion trends---人工智能工具检测全球时尚趋势

AI tool detects global fashion trends
人工智能工具检测全球时尚趋势

By Melanie Lefkowitz | October 29, 2019
梅勒妮·莱夫科维茨2019年10月29日

On the king’s birthday in Thailand – celebrated as Father’s Day – people often wear yellow shirts emblazoned with the word “DAD.”
在泰国，国王的生日——父亲节——人们通常穿印有“爸爸”字样的黄色衬衫。
On FreakNight in Seattle – a dance music event held around Halloween – revelers tend to wear sleeveless shirts, despite cool weather.
在西雅图的“怪诞之夜”(FreakNight)——万圣节前后举行的一场音乐舞蹈活动——狂欢者往往会穿无袖衬衫，尽管天气很冷。
And in September 2013, 1.2 million people – many clad in yellow shirts and blue scarves – linked arms to support Catalan independence from Spain.
2013年9月，120万人——其中许多人穿着黄色衬衫，戴着蓝色围巾——手挽着手，支持加泰罗尼亚从西班牙独立出来。
These are among the global insights gleaned from GeoStyle, a new artificial intelligence tool developed by Cornell researchers that scans millions of publicly available photos to effectively identify fashion trends around the world, as well as traditions and events with signature styles.
这是康奈尔大学(Cornell university)研究人员开发的新人工智能工具GeoStyle收集到的全球见解之一。GeoStyle扫描了数百万张可公开获取的照片，有效地识别世界各地的时尚趋势，以及带有标志性风格的传统和活动。
“A lot of people are continually uploading photos of themselves on the internet, because they want to share their style with their friends and the rest of the planet,” said Kavita Bala, professor and chair of computer science and senior author of “GeoStyle: Discovering Fashion Trends and Events,” presented at the International Conference on Computer Vision, Oct. 27 to Nov. 2 in Seoul, South Korea.
“很多人不断上传自己的照片在互联网上,因为他们想与他们的朋友分享他们的风格和其他行星,”巴拉的故事说,计算机科学的教授和主席和资深作者“GeoStyle:发现流行趋势和事件”,提出了计算机视觉国际会议,10月27日至11月2日在首尔,韩国。
“When you’re looking at these large collections of images, there are many, many things you can do to understand how people live,” Bala said.
“当你看着这些大量的图片时，你可以做很多很多事情来了解人们是如何生活的，”巴拉说。
“So we started off with the idea of looking at how people dress in different parts of the world: What are the commonalities, and what is distinctive to different areas?
“所以我们一开始就想看看世界不同地区的人是如何着装的:有什么共同点，不同地区的人有什么不同之处?”
If anthropologists could see this record 100 years from now, they would understand a lot about our time just by looking at these images and getting insights from them.”
如果人类学家能在100年后看到这一记录，他们就能通过这些图像了解我们这个时代的很多东西，并从中获得洞见。”
GeoStyle analyzes public Instagram and Flickr photos to map trends using computer vision and neural networks, a kind of artificial intelligence often used to sort images.
GeoStyle通过分析Instagram和Flickr上的照片，利用计算机视觉和神经网络绘制趋势图。
Its models help researchers understand existing trends in specific cities and around the world over time, and its trend forecasts are up to 20% more accurate than previous methods.
它的模型帮助研究人员了解特定城市和世界各地随时间变化的现有趋势，而且它的趋势预测比以前的方法精确20%。
For example, GeoStyle shows that year by year, more people wear black, but fewer people wear black in the summer than in the winter.
例如，GeoStyle的数据显示，每年穿黑色衣服的人越来越多，但夏天穿黑色衣服的人比冬天少。
The researchers also created a visualizer that allows users to view the popularity of a certain attribute – such as a pattern, hat or color – by city, over time.
研究人员还创建了一个可视化工具，允许用户查看某个属性(比如图案、帽子或颜色)随时间变化的受欢迎程度。
To refine the avalanche of data GeoStyle generates, the paper’s first author, Utkarsh Mall, a doctoral student in computer science, developed a framework to automatically identify spikes – short-term changes, some annual and some occurring once – that buck the longer-term trends.
为了细化GeoStyle生成的海量数据，论文的第一作者、计算机科学博士生乌特卡什?霍尔(Utkarsh Mall)开发了一个框架，自动识别与长期趋势相悖的峰值——短期变化、年度变化和一次性变化。
“We have all this cool machine learning technology that we’ve come up with to recognize images, but how do we make it useful?
“我们已经有了所有这些很酷的机器学习技术，我们已经想出了识别图像，但我们如何使它有用?”
” said co-author Bharath Hariharan, assistant professor of computer science.
合著者、计算机科学助理教授巴拉特?哈里哈兰表示。
“Our key question was, can we use this tool to automatically surface something we, as creators of this system, didn’t know before?”
“我们的关键问题是，我们能否利用这个工具来自动呈现一些我们作为这个系统的创造者之前不知道的东西?”
In fact, the model was able to identify dozens of short-term style changes corresponding to events around the world, including many the researchers weren’t aware existed, such as Songkran in Bangkok, a festival celebrated in April on the Thai New Year.
事实上，该模型能够识别出几十种与世界各地的事件相对应的短期风格变化，其中包括许多研究人员不知道的存在，比如曼谷的泼水节(Songkran)，这个节日在4月的泰国新年举行。
Once it identifies a spike, the tool employs a text analysis based on photo captions to figure out what it might mean.
一旦它识别出一个峰值，该工具就会基于图片说明进行文本分析，以找出它可能的含义。
The researchers at first thought that the spike in sleeveless shirts in Seattle had to do with Halloween, because it occurs around that time, but the text associated with the photos contained the word “Freaknight,” which helped them identify it as a distinct celebration.
研究人员起初认为，西雅图无袖衬衫的激增与万圣节有关，因为它大约发生在那个时候，但与照片相关的文字中包含了“怪诞之夜”(Freaknight)这个词，这帮助他们将其识别为一个独特的庆祝活动。
“This was an example where analyzing the text really made a difference,” Hariharan said.
Hariharan说:“这是一个文本分析真正起到作用的例子。”
The project builds on StreetStyle, launched in 2017 by Bala and GeoStyle co-authors Noah Snavely, associate professor of computer science at Cornell Tech, and Kevin Matzen, Ph.D. ’15, of Facebook.
该项目建立在StreetStyle的基础上，由Bala和GeoStyle的合著者诺亚·斯纳维里(诺亚·斯纳维里是康奈尔理工学院的计算机科学副教授)和凯文·马森博士(15年的Facebook)于2017年发起。
StreetStyle detects trends based on time and location by analyzing millions of images.
StreetStyle通过分析数百万张图片，根据时间和地点来检测趋势。
The team is currently working with Denise Green, assistant professor of fiber science and apparel design, and other fashion experts at the College of Human Ecology, to improve their model.
该团队目前正与纤维科学和服装设计助理教授丹尼斯·格林(Denise Green)以及人类生态学院(College of Human Ecology)的其他时尚专家合作，以改进他们的模型。
The tool can do a better job spotting trends if it knows what it’s looking for, Bala said.
Bala说，如果这个工具知道自己在寻找什么，它可以更好地发现趋势。
“An expert can identify important visual features in a very different way than we can just by mining it,” she said.
她说:“专家识别重要的视觉特征的方式与我们仅仅通过挖掘是非常不同的。”
For example, she said, a student pointed out that the data showed the evolution of trucker hats from an accessory worn by farmers to one appearing on fashion runways to widespread popularity.
她说，例如，一名学生指出，数据显示，卡车司机帽从农民戴的配饰，到出现在时尚t台上，再到大受欢迎，经历了演变。
“One of our follow-ups from this work is improving the technology so that if you add a little expert information, you can improve the recognition and get an even finer-grained understanding,” Bala said.
巴拉说:“我们这项工作的后续工作之一是改进技术，这样如果你添加一点专家信息，你就可以提高认可度，获得更细致的理解。”
Other potential applications for the technology include scanning satellite imagery to track changes in land use patterns, the researchers said.
研究人员说，这项技术的其他潜在应用包括扫描卫星图像，以跟踪土地利用模式的变化。
The study was partly funded by the National Science Foundation and an Amazon Research Award.
这项研究得到了美国国家科学基金会(National Science Foundation)和亚马逊研究奖(Amazon Research Award)的部分资助。