How computer vision will help Amazon customers shop online

Three papers at CVPR present complementary methods to improve product discovery.

The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) is the premier conference in the field of computer vision, and the Amazon papers accepted there this year range in topic from neural-architecture search to human-pose tracking to handwritten-text generation.

But retail sales are still at the heart of what Amazon does, and three of Amazon’s 10 CVPR papers report ways in which computer vision could help customers shop for clothes.

One paper describes a system that lets customers sharpen a product query by describing variations on a product image. The customer could, for instance, alter the image by typing or saying “I want it to have a light floral pattern”.

A second paper reports a system that suggests items to complement those the customer has already selected, based on features such as color, style, and texture.

The third paper reports a system that can synthesize an image of a model wearing clothes from different product pages, to demonstrate how they would work together as an ensemble. All three systems use neural networks.

Outfit composite.png
A query image (left) is combined with images from different product pages to produce a synthetic composite (right).

Visiolinguistic product discovery

Using text to refine an image that matches a product query poses three main challenges. The first is finding a way to fuse textual descriptions and image features into a single representation. The second is performing that fusion at different levels of resolution: the customer should be able to say something as abstract as “Something more formal” or as precise as “change the neck style”. And the third is training the network to preserve some image features while following customers' instructions to change others.

Yanbei Chen, a graduate student at Queen Mary University of London, who was an intern at Amazon when the work was done; Chen’s advisor, professor of visual computation Shaogang Gong; and Loris Bazzani, a senior computer vision scientist at Amazon, address these challenges with a neural network that’s trained on triples of inputs: a source image, a textual revision, and a target image that matches the revision.

Essentially, the three inputs pass through three different neural networks in parallel. But at three distinct points in the pipeline, the current representation of the source image is fused with the current representation of the text, and the fused representation is correlated with the current representation of the target image.

Because the lower levels of a neural network tend to represent lower-level features of the input (such as textures and colors) and higher levels higher-level features (such as sleeve length or tightness of fit), using this “hierarchical matching” objective to train the model ensures that it can handle textual modifications of different resolutions.

Visiolinguistic architecture.png
A new system that enables textual modification of product images fuses visual and linguistic information at three different levels of a neural network, to accommodate different degrees of textual granularity.
Apparel images from the Fashion IQ data set (Xiaoxiao Guo, et al.), used with permission under the Community Data License Agreement.

Each fusion of linguistic and visual representations is performed by a neural network with two components. One component uses a joint attention mechanism to identify visual features that should be the same in the source and target images. The other is a transformer network that uses self-attention to identify features that should change.

In tests, the researchers found that the new system could find a valid match to a textual modification 58% more frequently than its best-performing predecessor.

Complementary-item retrieval

In the past, researchers have developed systems that took outfit items as inputs and predicted their compatibility, but these systems were not optimized for large-scale data retrieval.

Amazon applied scientist Yen-Liang Lin and his colleagues wanted a system that would enable product discovery at scale, and they wanted it to take multiple inputs, so that a customer could, for instance, select shirt, pants, and jacket and receive a recommendation for shoes.

The network they devised takes as inputs any number of garment images, together with a vector indicating the category of each — such as shirt, pants, or jacket. It also takes the category vector of the item the customer seeks.

The images pass through a convolutional neural network that produces a vector representation of each. Each representation then passes through a set of “masks”, which attenuate some representation features and amplify others.

The masks are learned during training, and the resulting representations encode product information (such as color and style) relevant to only a subset of complementary items. That is, some of the representations that result from the masking — called subspace representations — will be relevant to shoes, others to handbags, others to hats, and so on.

Complementarity network.png
The architecture of the neural network used for complementary-item retrieval. From vectors representing the product categories of both input items and a target item, the network produces a set of weights (w1 – wk) that indicate which input-item features should be prioritized in selecting a complementary item.

In parallel, another network takes as input the category for each input image and the category of the target item. Its output is a set of weights, for prioritizing the subspace representations.

The network is trained using an evaluation criterion that operates on the entire outfit. Each training example includes an outfit, an item that goes well with that outfit, and a group of items that do not.

Once the network has been trained, it can produce a vector representation of every item in a catalogue. Finding the best complement for a particular outfit is then just a matter of looking up the corresponding vectors.

In experiments that used two standard measures in the literature on garment complementarity — fill-in-the-blank accuracy and compatibility area under the curve — the researchers’ system outperformed its three top predecessors, while enabling much more efficient item retrieval.

Virtual try-on network

Previously, researchers have trained machine learning systems to synthesize images of figures wearing clothes from different sources by using training data that featured the same garment photographed from different perspectives. But that kind of data is extremely labor intensive to produce.

Senior applied scientist Assaf Neuberger and his colleagues at Amazon’s Lab126 instead built a system that can be trained on single images, using generative adversarial networks, or GANs. A GAN has a component known as a discriminator, which, during training, learns to distinguish network-generated images from real images. Simultaneously, the generator learns to fool the discriminator.

The researchers’ system has three components. The first is the shape generation network, whose inputs are a query image, which will serve as the template for the final image, and any number of reference images, which depict clothes that will be transferred to the model from the query image.

Complementarity system.png
Amazon researchers’ “virtual try-on network” uses a three-step process to synthesize an image of a model wearing garments from different sources.

In preprocessing, established techniques segment all the input images and compute the query figure’s body model, which represents pose and body shape. The segments selected for inclusion in the final image pass to the shape generation network, which combines them with the body model and updates the query image’s shape representation. That shape representation passes to a second network, called the appearance generation network.

The architecture of the appearance generation network is much like that of the shape generation network, except that it encodes information about texture and color rather than shape. The representation it produces is combined with the shape representation to produce a photorealistic visualization of the query model wearing the reference garments.

The third component of the network fine-tunes the parameters of the appearance generation network to preserve features such as logos or distinctive patterns without compromising the silhouette of the model.

The outputs of the new system are more natural looking than those of previous systems. In the figure below, the first column is the query image, the second the reference image, the third the output of the best-performing previous system, and the fourth and fifth the outputs of the new system, without and with appearance refinement, respectively.

From left to right: query samples, reference samples, the previous system’s output, and the new system’s outputs, without and with the appearance refinement network.

About the Author
Larry Hardesty is a science writer at Amazon. Previously, he was managing editor of the Boston Book Review, a senior editor at MIT Technology Review, and the computer science writer at the MIT News Office.

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The Workforce Staffing team is responsible for our hourly workforce: Who do we hire? How should we hire them? How do we make sure the right candidate gets the right role at the right time, and how do we do this efficiently? In 2019, WFS hired hundred of thousands of hourly associates across NA and EU and will receive over millions of job applications for employment. This role is part of the Workforce Intelligence Team, tasked acquiring, modeling and visualizing all the data required to report out on performance metrics such as fill rates and funnel statistics, and forecasting hiring volumes to predict hiring risks and to support internal capacity planning.This role will be responsible for challenging the status quo by analyzing and identifying factors that predict and forecast success and failure in Amazon's current and future labor markets. You will own the analysis across multiple data sources to predict hiring risk before it occurs. The ideal candidate will possess strong analytical and technical expertise to build analytic solutions to drive business improvements at scale.Key Responsibilities- Identify key market and company factors that introduce risk to meeting labor orders across our full time and part time hourly associate populations- Evaluate effectiveness and forecast ROI on key decisions to increase Amazon's employment value proposition- Build models that improve hiring funnel efficiency at scale- Influence key business decisions in potentially adjusting labor plans, shift structures, compensation and/or benefits, and other factors to increase Amazon's appeal across a diverse workforce- Build predictive models to anticipate labor order volumes and fluctuations before they occur
US, NY, New York
Amazon’s Advertising Audience and Media Insights team (AMI) is looking for a motivated and experienced Applied Science Manager to own and lead a growing science team within our org. Our team owns the product, science, technology and deployment roadmap for advanced analytics and media planning products across our Insights and Performance Team. Analytics and media planning is core to Amazon’s growth, as it helps our suppliers drive awareness, consideration, and purchase of their products by hundreds of millions of consumers around the world, and generates revenue which helps us lower prices and invest in improvements to our customer experience. We are a highly motivated, collaborative and fun-loving team with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities.You are the single threaded owner of the Media Insights Science program. You are customer obsessed and have sharp business acumen to build a product vision and roadmap that contributes to the success of our advertisers. You are a technical leader with track record of building and growing applied science and engineering teams. You experiment, develop proof of concept, and ship software. You own the day to day maintenance and evolution of systems that serve the needs of our large and growing base of advertisers. We are looking for a leader who can thrive in a startup environment, thinks big, can move fast, and wants to change the way customers use data to drive business success. If you excel at proposing and developing unique analytical insights and are passionate about creating the future, come join us as we work hard, have fun, and make history.You will:* Own technical vision and direction: You identify and mitigate risks, construct project schedules, and prepare status reports; you embrace performance metrics and measurement techniques because they help you assess how well system-related services are running.* Grow your team: You'll be accountable for growing a team featuring a mix of scientists and engineers that deliver results. This means you'll wear a lot of hats -- from developing scientific models to leading technical discussions, recruiting, establishing processes, and so on. You'll be an example of Amazon's leadership principles and work to grow more leaders within your group. You will manage a fast-growing team that spans multiple locations.* Collaborate on product direction: You’ll build and maintain strong relationships between your team and partner disciplines (Product, User Experience, QA) to ensure that we're focused on delivering the right product for customers.* Lead beyond your team: You will be a key leader within Amazon’s advertising organization. You will contribute to the overall growth of our product development organization. You'll share your best practices and technical acumen in order to drive technology decisions across our organization.You are:* Pragmatic and iterative in developing models and building software: you have an ability to simplify and get things done quickly with a demonstrated track record of building and delivering software and working effectively with external and internal teams.* Highly analytical and data-driven: You solve problems in ways that can be backed up with verifiable data. You focus on driving processes, tools, and statistical methods which support rational decision-making.* Technically fearless: You aren't satisfied by performing 'as expected' and push the limits past conventional boundaries.* Team obsessed: You find and cultivate talent to be capable of achieving outstanding results. You help develop the creative atmosphere to let engineers innovate, while holding them accountable for making smart decisions and delivering results.* Comfortable with ambiguity: You're able to explore new problem spaces with unique constraints and thus non-obvious solutions; you’re quick to identify any gaps in the team and the right person to fill them to best deliver value to customers.* Solutions; you’re quick to identify any gaps in the team and the right person to fill them to best deliver value to customers.