digital marketing

Posted On : 21st May, 2020 by Rohit Purohit

Coronavirus (COVID-19) pandemic has gripped the world in a chokehold and in a span of a few months shattered a multitude of industries across the planet. As we move forward, we will need to adapt and change the manner in which we have previously conducted businesses as the current methodologies and supply chain mechanisms are rendered useless. 

Everyone is aware that big tech has taken on an outsized role in our lives. Now it’s only natural that it’s taking on an outsized role in the coronavirus response.  

Facebook happens to be one of the biggest technology companies on the planet and starting today it is introducing an array of features on its ‘Facebook’ and ‘Instagram’ platform to help small and medium businesses fight the COVID-19 pandemic. The pandemic’s economic fallout is already hurting and killing off many small businesses — businesses that usually post and advertise on Facebook. So, the company has a stake in helping those businesses survive in any way it can. In a Facebook Live session held on 20/May/2020, CEO Mark Zuckerberg described this as a way to help businesses suffering in the wake of COVID-19, though he acknowledged it will not “undo all the economic damage. 

The company has announced the launch of Shops, a way for businesses to set up free storefronts on Facebook and Instagram. The shops, which will be powered by third-party services, including Shopify, BigCommerce, and Woo, are designed to turn the social network into a top-tier shopping destination. 

In a live stream, CEO Mark Zuckerberg said expanded e-commerce would be important to begin rebuilding the economy while the pandemic continues. To accomplish this Facebook is launching what it’s calling a “universal product recognition model” that uses artificial intelligence to identify consumer goods, from furniture to fast fashion to fast cars. It’s the first step toward a future where the products in every image on its site can be identified and potentially shopped for. 

Product recognition is the first in a slew of artificial intelligence (AI) powered updates coming to its e-commerce platforms in the near future. Eventually, these will combine AI, augmented reality, and even digital assistants to create what is referred as a “social-first” shopping experience. Moreover, it also today launched a feature called Shops, that lets small businesses set up free storefronts on Facebook and Instagram. 

This is not the first time that emerging technologies like artificial intelligence and machine learning are being utilized for e-commerce. Amazon in the past built its own artificial intelligence powered fashion assistant with the Echo Look, which now is little heard-of. And utilizing computer vision to identify and shop for products has been a reality since the introduction of Amazon Fire Phone. Meanwhile, online shopping platforms like eBay already use AI to speed up the process of listing items for sale, and Amazon is one of a number of firms that’s launched its own so-called “Shazam for clothes” using machine learning. 

Facebook’s Artificial Intelligence 

Finding products that suits your choice can be a challenge even when done in person at a brick-and-mortar store. Add to that the vagaries of online commerce, where you can only see a stock representation of what you want, can make for a frustrating, and occasionally ill-fitting, shopping experience — especially when purchasing items second-hand. That’s an issue that Facebook’s looks to address with its new GrokNet computer vision system that can transform virtually any photo into a shopping opportunity. 

GrokNet is designed specifically for shopping and is capable of identifying the attributes — color, texture, pattern or style, for example — of the items it sees. Typically, machine vision systems are only adept at picking out one type of object, so a system designed to recognize various styles of shoe is going to struggle with identifying specific models of a motorcycle 

Facebook says that GrokNet, which can detect exact, similar (via related attributes), and co-occurring products across billions of photos, performs searches and filtering on Marketplace at least twice as accurately than the algorithm it replaced. For instance, it’s able to identify 90% of home and garden listings compared with Facebook’s text-based attribution systems, which can only identify 33%. In addition to generating tags for colors and materials from images before Marketplace sellers list an item, as part of a limited test, it’s tagging products on Facebook Pages when Page admins upload a photo. 

In the course of training GrokNet, Facebook has said it used real-world seller photos with “challenging” angles along with catalog-style spreads. To make it as inclusive as possible for all countries, languages, ages, sizes, and cultures, it sampled examples of different body types, skin tones, locations, socioeconomic classes, ages, and poses. 

Rather than manually annotate each image with product identifiers, which would have taken ages — there are 3 million possible identifiers — Facebook developed a technique to automatically generate additional identifiers using GrokNet as a feedback loop. Leveraging an object detector, the approach identifies boxes in images surrounding likely products, after which it matches the boxes against a list of known products to keep matches within a similarity threshold. The resulting matches are added to the training set. 

Facebook’s SLAM technique also combines observations from frames to obtain a sparse point cloud, which consists of the most prominent features from any given captured scene. This cloud serves as guidance to the camera poses that correspond to viewpoints best representing objects in 3D; images are distorted in such a way that they look like they were taken from the viewpoints. A heuristic outlier detector finds key points that could introduce distortions and discards them, while similarity constraints make the featureless parts of the reconstructions more rigid and out-of-focus areas look more natural. 

Beyond 3D reconstructions, Facebook says that it will soon draw on its Spark AR platform checkout to allow customers to see how items look in various places. (Already, brands like Nyx, Nars, and Ray-Ban use it in Facebook Ads and Instagram to power augmented reality “try-on” experiences.) The company plans to support try-on for a wider variety of items — including home decor and furniture — across apps and services including Shops, Facebook’s feature that enables businesses to sell directly through the network. 


To imbue services like Marketplace with the ability to automatically isolate clothing products within images, Facebook developed a segmentation technology it claims achieves state-of-the-art performance compared with several baselines. The tech — an “operator” called Instance Mask Projection — can spot items like wristbands, necklaces, skirts, and sweaters photographed in uneven lighting or partially obscured, or even shown in different poses and layered under other items like shirts and jackets. 

Instance Mask Projection detects a clothing product as a whole and roughly predicts its shape. This prediction serves as a guide to refine the estimate for each pixel, allowing global information from the detection to be incorporated. The predicted instance maps are projected into a feature map that’s used as input for semantic segmentation. According to Facebook, this design makes the operator suitable for clothing parsing (which involves complex layering, large deformations, and non-convex objects) as well as street-scene segmentation (overlapping instances and small objects). 

Facebook says it’s training its product recognition systems with the operator across dozens of product categories, patterns, textures, styles, and occasions, including lighting and tableware. It’s also enhancing the tech to detect objects in 3D photos, and in a related effort, it’s developing a body-aware embedding to detect clothing that might be flattering for a person’s shape. 

Toward an Artificial Intelligence Forward Business Future 

AI in the past few years has found uses in a myriad of industries its goal is to one day combine these disparate approaches into a system that can serve up product recommendations on the fly, matched to individual tastes and styles. It envisions an assistant that can learn preferences by analyzing images of what’s in a person’s wardrobe, for instance, and that allows the person to try favorites on self-replicas and sell apparel that others can preview. 

To this end, Facebook says its researchers are prototyping an “intelligent digital closet” that provides not only suggestions based on planned activities or variables as diverse as the weather, but also inspiration informed by individual products and aesthetics. 


Facebook anticipates that new systems will ultimately be required to adapt to changing trends and preferences, ideally systems that learn from feedback on images of potentially desirable products.    

With this initiative Facebook aims to propel any seller, no matter their size or budget can bring their business online and connect with customers wherever and whenever it’s convenient for them. People can find Facebook Shops on a business’ Facebook Page or Instagram profile, or discover them through stories or ads. 

Artificial intelligence (AI) is constantly modifying the world of online shopping. Today, AI is changing the way in which e-commerce stores operate and provide services to their customers. Right from offering virtual buying assistants to creating personalized shopping experience, AI is refining online shopping experience for both retailers and customers. The technology also provides new ways to analyze Big Data and helps e-commerce companies to engage with their customers on a new level and create superior client experiences. Achieving highly personalized customer experience holds the key to the success of online marketers. 

The AI applications can analyze consumer data to predict future purchasing patterns and make product recommendations, based on the browsing patterns. 

Before staggering due to the COVID-19 pandemic eCommerce was claimed to be a $2 trillion market, despite the recent slowdown faced a abrupt damage to businesses around the world in the bounce back we expect Artificial Intelligence (AI) to push this number even bigger than what it was. 


  • Engagdet 
  • TechCrunch 
  • Business Insider