Analyzing fashion trends is essential in the fashion industry. Current fashion forecasting firms, such as WGSN, utilize the visual information from around the world to analyze and predict fashion trends. However, analyzing fashion trends is time-consuming and extremely labor intensive, requiring individual employees' manual editing and classification. To improve the efficiency of data analysis of such image-based information and lower the cost of analyzing fashion images, this study proposes a data-driven quantitative abstracting approach using an artificial intelligence (A.I.) algorithm. Specifically, an A.I. model was trained on fashion images from a large-scale dataset under different scenarios, for example in online stores and street snapshots. This model was used to detect garments and classify clothing attributes such as textures, garment style, and details for runway photos and videos. It was found that the A.I. model can generate rich attribute descriptions of detected regions and accurately bind the garments in the images. Adoption of A.I. algorithm demonstrated promising results and the potential to classify garment types and details automatically, which can make the process of trend forecasting more cost-effective and faster.

Circular fashion is part of the circular economy, an economic system that at its core is embedded with an ideology of reuse, recycle and refurbish in order to eliminate waste, stop items from going into landfills, and extend the lifecycle of products by keeping them in use and in circulation. The fashion industry is notorious for its wasteful and environmentally damaging practices accounting for over 10% of global carbon emissions a percentage which is slated to increase to 24% of the global carbon budget by the year 2050 at current demand. Much of this is due to the synthetic fibers and fabrics primarily used in fast fashion, 70 million barrels of oil are used to produce polyester every year and wasteful practices exacerbate the impact, it turns out that the equivalent of one garbage truck of textiles is landfilled or incinerated every second! Fresh on the heels of a $175 million raise, thredUP is poised to capitalize on the growing $24 billion second-hand market through its use of artificial intelligence to bring efficiencies and scale to every area of its operations, while fueling the circular fashion trend among traditional retail brands with the launch of its "resale as a service" offering. The company's mission is to inspire a new generation of shoppers to think second hand first, keeping clothing out of landfills so that people can look great without being part of the problem.

If you're a savvy shopper and constantly hunting down the newest fashion trends via social media then this AI fashion shop could be right up your street. In a first of its kind, the Trending Store pop-up in Westfield London is using AI to track social media fashion trends in real time. The shop will exclusively only stock the 100 trending pieces on social media and they will change each day dependent on what is most popular. The fashion boutique will then allow customers to browse and buy the full range of clothing and accessories in store with all proceeds going towards charity Save the Children. A pop up in Westfield London are using AI trends in real time based on what's trending on social media So how does it work?

It would be nearly impossible for one person – or even a dedicated team – to tease out meaningful trends and insights from such an onslaught of visual data. For an AI (properly trained with the right algorithms), it's a piece of cake, according to Kavita Bala, chair of the computer science department at Cornell University. She and her team used artificial intelligence (AI) to create a map of style trends and influencers by analyzing 14.5 million photos of people shared publicly on social media. Bala's StreetStyle project can answer questions like: How many people wear black in Los Angeles today, compared with two years ago? Or, where in the world is the hijab most prevalent?

With the proliferation of social media, fashion inspired from celebrities, reputed designers as well as fashion influencers has shortened the cycle of fashion design and manufacturing. However, with the explosion of fashion related content and large number of user generated fashion photos, it is an arduous task for fashion designers to wade through social media photos and create a digest of trending fashion. This necessitates deep parsing of fashion photos on social media to localize and classify multiple fashion items from a given fashion photo. While object detection competitions such as MSCOCO have thousands of samples for each of the object categories, it is quite difficult to get large labeled datasets for fast fashion items. Moreover, state-of-the-art object detectors do not have any functionality to ingest large amount of unlabeled data available on social media in order to fine tune object detectors with labeled datasets. In this work, we show application of a generic object detector, that can be pretrained in an unsupervised manner, on 24 categories from recently released Open Images V4 dataset. We first train the base architecture of the object detector using unsupervisd learning on 60K unlabeled photos from 24 categories gathered from social media, and then subsequently fine tune it on 8.2K labeled photos from Open Images V4 dataset. On 300 X 300 image inputs, we achieve 72.7% mAP on a test dataset of 2.4K photos while performing 11% to 17% better as compared to the state-of-the-art object detectors. We show that this improvement is due to our choice of architecture that lets us do unsupervised learning and that performs significantly better in identifying small objects.

Watson won't be wearing anything fancy to the Grammys this weekend, but that's not going to keep it from judging everyone else's outfit. For the 60th anniversary of the music awards, the Recording Academy is partnering with IBM to bring its artificial intelligence to the red carpet. On Sunday night, IBM will deploy its AI platform to analyze videos and photos of nominees and attendees as they arrive at the ceremony in New York. In addition to identifying each person, Watson will be able to understand styles, learn about this year's fashion trends and compare them to those of previous years. People will then curate those findings, along with a selection of the many photos and videos taken by photographers, and upload them to the Grammys website for fans to learn more about their favorite musicians along with those honored decades ago.

Technology has evolved a lot since then, but closets have been largely untouched by innovation. Now, that's starting to change. "If algorithms do their job well, people will spend less time thinking about what to wear," said Ranjitha Kumar, an assistant professor in the Department of Computer Scie...

In the future, you could have a personalised AI stylist that knows what you like to wear and decides what your next outfit should be. Researchers are working on a neural network that could create'predictive fashion' using CGI images of items of clothing that might look good. This system could be used by retailers to give people a personalised shopping experience as well as helping predict broader fashion trends. Researchers at the retail giant are working on machine-learning systems that could potentially shape fashion trends of the future. The system relies on a tool called GAN (generative adversarial network).

Artificial intelligence might just spawn a whole new style trend: call it "predictive fashion." In a paper published on the ArXiv, researchers from the University of California, San Diego, and Adobe have outlined a way for AI to not only learn a person's style but create computer-generated images of items that match that style. The system could let retailers create personalized pieces of clothing, or could even be used to help predict broader fashion trends. First, the researchers trained a convolutional neural network (CNN) to learn and classify a user's preferences for certain items, using purchase data scraped from Amazon in six categories: shoes, tops, and pants for both women and men. This type of recommender model is common in the online retail world, usually showing up in an "Other items you might like" area at the bottom of a page.

There have been a number of buzzwords and defining technology trends in retail over the last decade: from Big Data, to omnichannel, and the ubiquitous, omni-present Cloud. And now the internet of things (IoT) and artificial intelligence (AI) have seemingly become the latest crazes and talk of the town. Forrester expects investment in AI to triple this year. By 2020, 85% of customer interactions will be managed by AI according to research by Gartner. The value of AI is estimated to be worth $36.8bn globally by 2025 predicts US market intelligence firm Tractica.