AI+Fashion, How Does AI Knows the Hot Styles and Fashion Trends of the Next Season?

Sometimes, new fashion trends seem to appear suddenly, but in reality, these fashions are usually the product of months or even years of careful observation and planning by behind-the-scenes fashion trend predictors.

Those who are keen on trends will notice the trend of fashion shows and celebrity wear. They collect data on politics, entertainment, environment, technology, and consumer behavior. Cultural observation is a starting point, allowing people to infer what colors, contours, and fabrics will become the next fashion trend two years in advance.

Where does fashion come from?

Carrera Kurnik, the chief editor of the column from Fashion Snoops, a trend forecasting company, said: “Fashion is always a response to the environment. The economic environment, emotional environment, political environment as well as all the different things around us all affect our fashion outlook, including the way we dress, how we want others to see us, and how we show others who we are.” Fashion trend forecasters will observe any changes in these environments.

According to Francesca Muston, vice president of fashion at forecasting company WGSN, the real analysis begins after a series of similar observations. After identifying a cultural trend, the analyst translates it into a corresponding fashion dynamic. In addition to getting a steady stream of information from potential consumers on social media, forecasting companies also collect a large amount of historical data (show photos, fashion magazine archives, and their own research).

In the past, almost all trends came from the elite of the fashion industry. But social media and influencer culture have changed this. Kurnik said that the Internet has indeed changed the source of fashion information to a large extent.

How can artificial intelligence become an expert in fashion trend forecasting?

Now, in addition to analyzing social media, many companies use artificial intelligence programs to pursue new trends. For example, Fashion Snoops uses AI to search for buzzwords and novel slang that have potential trends on the Internet.

IBM’s Watson AI can analyze thousands of images from fashion shows and analyze insights into which colors and patterns retailers should look for in the upcoming season. The algorithm can ignore irrelevant data, such as the type of background and the skin tone of the model, and then find and record the prominent colors in each image, finally getting data about the frequency of each color.

It can also perform similar analyses on fabric patterns and find similarities between different fashion shows. It is impossible for fashion trend predictors to analyze so much data for the next season in time, using artificial intelligence to do this heavy work allows fashionistas to focus on finding upcoming trends in less traditional fields such as movies, television, and even politics.

Researchers say that the power of AI in trend prediction does not stop there. As we all know, the fashion industry is an industry that brings a lot of trouble to the ecological environment, accounting for 10% of global carbon emissions, putting up to 9 million tons of textiles into landfills every year. To alleviate this problem, AI systems can help clothing manufacturers predict the demand for each style. When a new product hits the market, if AI can predict the sales of that product two months in advance, it can help minimize unsold inventory. Reducing waste could be an important step in the sustainable development of fashion brands.

High-quality labeled data

With the acceleration of the commercialization of AI and the application of AI technologies such as assisted driving and customer service chatbot in all walks of life, the expectation of data quality in the special scenarios is getting higher and higher. High-quality labeled data would be one of the core competitiveness of AI companies.

If the general datasets used by the previous algorithm model are coarse grains, what the algorithm model needs at present is a customized nutritious meal. If companies want to further improve certain models’ commercialization, they must gradually move forward from the general dataset to create the unique one.

NLP Service

We provide different types of NLP in E-commerce, Retail, Search engines, Social Media, etc. Our service includes Voice Classification, Sentiment Analysis, Text Recognition and Text Classification(Chatbot Relevance).

End

Outsource your data labeling tasks to bytebridge.io, you can get the high-quality ML training datasets cheaper and faster!

• Free Trial Without Credit Card: you can get your sample result in a fast turnaround, check the output, and give feedback directly to our project manager.
• 100% Human Validated
• Transparent & Standard Pricing: clear pricing is available(labor cost included)

Why not have a try?

source: https://baijiahao.baidu.com/s?id=1692197701977257005&wfr=spider&for=pc