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.

Research areas
About the Author
Larry Hardesty is the editor of the Amazon Science blog. Previously, he was a senior editor at MIT Technology Review and the computer science writer at the MIT News Office.

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Robotics Applied Scientist Intern (Westborough, North Reading MA)Are you a MS or PhD student interested in the fields of Computer Vision, Robotics, Machine Learning and Deep Learning and capable of diving deep into hard technical problems and coming up with insightful solutions that enable successful products? Are you unafraid of taking on a mind-bending challenge that really will improve peoples' lives in a meaningful way? Would you love to get access to large datasets with billions of images and video to build large-scale machine learning systems? Are you a finisher who can deliver robust, production-quality code that solves complex, real-world problems in ways that delight customers?If this describes you, come join the Computer Vision teams at Amazon Robotics. Our teams are using computer vision, machine learning, and real-time and distributed systems to create solutions for problems that impact millions of people. A Computer Vision Scientist at Amazon Robotics will translate business and functional requirements into quick prototypes or proofs of concept. Comfort with a high degree of ambiguity and ability to solve problems that haven’t been solved to scale before are essential.Amazon Robotics is seeking talented and motivated interns to develop solutions for controling and optimizing mobile-robotic fulfillment systems used by operations. Our problems span autonomous movement, object identification, grasping, path planning, and action understanding. This position will report to the Amazon Robotics functional manager most closely aligned with the candidate’s skillset and project work. Each intern will have an assigned technical mentor throughout the internship, as well as 1-1 guidance from their manager, and will work closely with other engineers.Amazon Robotics internship opportunities will be based virtually or in the Greater Boston Area, in our two state-of-the-art facilities in Westborough and North Reading, MA. Both campuses provide a unique opportunity for interns to have direct access to robotics testing labs and manufacturing facilities.Amazon internships are full-time (40 hours/week) for 12 or more consecutive weeks with start dates between May and June 2021.
US, NY, New York
Amazon is building a world class advertising business and defining and delivering a collection of self-service performance advertising products that drive discovery and sales of merchandise. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving 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.Sponsored Products helps merchants, retail vendors, and brand owners succeed via native advertising that grows incremental sales of their products sold through Amazon. The Sponsored Products Ad Marketplace organization optimizes the systems and ad placements to match advertiser demand with publisher supply using a combination of machine learning, big data analytics, ultra-low latency high-volume engineering systems, and quantitative product focus. Our systems and algorithms operate on one of the world's largest product catalogs, matching shoppers with products - with a high relevance bar and strict latency constraints. Our goals are to help buyers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and to build a major, sustainable business that helps Amazon continuously innovate on behalf of all customers.As an Applied Scientist for the Sponsored Products Detail Page Allocation and Pricing team, you develop systems which make the final decision on which ads to show, where to place them on the page and how many ads to place. This also includes selection of various themes that would appear in detail pages. This is a challenging technical and business problem, which requires us to balance the interests of advertisers, shoppers, and Amazon. You'll develop a data-driven product strategy to define the right quantitative measures of shopper impact, using this to evaluate decisions and opportunities. You'll balance a portfolio of pragmatic and long-term investments to drive long term growth of the ads and retail businesses. You'll develop real-time algorithms to allocate billions of ads per day in advertising auctions.Job Responsibilities:· Build machine learning models and utilize data analysis to deliver scalable solutions to business problems.· Run A/B experiments, gather data, and perform statistical analysis.· Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving.· Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production.· Research new machine learning approaches.Impact and Career Growth:In this role you will have significant impact on this team as well as drive cross team projects that consist of Applied Scientists, Data Scientists, Economists, and Software Development Engineers. This is a highly visible role that will help take our products to the next level. You will work alongside many of the best and brightest science and engineering talent and the work you deliver will have a direct impact on customers and revenue!Why you love this opportunity:Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving 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.Team video ~
US, NY, New York
Amazon is looking for a passionate, talented, and inventive Applied Scientists with a strong machine learning background to help build industry-leading Speech and Language technology.Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Automatic Speech Recognition (ASR), Machine Translation (MT), Natural Language Understanding (NLU), Machine Learning (ML) and Computer Vision (CV).As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services that make use of speech and language technology. You will gain hands on experience with Amazon’s heterogeneous speech, text, and structured data sources, and large-scale computing resources to accelerate advances in spoken language understanding.We are hiring in all areas of human language technology: ASR, MT, NLU, text-to-speech (TTS), and Dialog Management, in addition to Computer Vision.Inclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, MA, North Reading
Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun.We are looking for world-class scientists and engineers to explore new ideas, invent new approaches, and develop new solutions.If you have expertise in Computer Vision, with extensive industry experience and have:· the ability to think big and conceive of new ideas and novel solutions;· the insight to correctly identify those worth exploring;· the hands-on skills to quickly develop proofs-of-concept;· the rigor to conduct careful experimental evaluations;· the discipline to fast-fail when data refutes theory;· and the fortitude to continue exploring until your solution is foundcome join us to invent the future and change the world.
CA, ON, Toronto
Alexa is the name of the Amazon cloud-based voice service that powers the Echo, Echo Dot, Echo Show and more. Just ask Alexa for information, music, news, weather, and more. And now you can send a message, make a call, play announcements, or drop in on your closest friends & family via Alexa devices or the Alexa app!It’s still Day One for the Alexa Communications team – we have a lot to innovate and build to make communication through Alexa devices a magical experience. We’re working hard, having fun, and making history; come join us! The Alexa communications team is working to become the most natural way for people to communicate, and the challenge ahead is significant. We're a high energy, fast growth business excited to have the opportunity to define the future of voice-controlled communications, make Alexa even more useful, and delight customers around the world.We are seeking talented Audio DSP Engineers to join our Comms Media teams. These teams provide the mechanisms and services that allow our Customers to connect and communicate with each other around the world. In this role you will have an enormous opportunity to make a large impact on the design, architecture, and implementation of cutting edge products used every day, by people you know.The ideal candidate will be passionate about an opportunity to build elegant systems in the most efficient ways, pushing the limits of current technology and challenging the status quo.Key responsibilities include:· Develop and launch core product features· Influence our overall strategy by helping define product features, drive the system architecture, and spearhead the best practices that enable a quality product· Create world class software and Alexa experiences· Interact with cross-functional engineering teams across the company· Dive into and take ownership of mission critical software puzzles· Participate in and drive design reviews· Contribute significantly to key communications quality of Alexa· Innovate, prototype, and create new technology· Design and architect a highly performant, critical application at large scale· Work in an Agile environment to deliver high quality software
US, WA, Seattle
Customer Trust & Partner Support (CTPS) is responsible for creating a trustworthy shopping experience across Amazon stores worldwide by protecting customers, brands, selling partners and Amazon from fraud, counterfeit, and abuse as well as empowering, providing world‐class support, and building loyalty with Amazon’s millions of selling partners. We value individual expression, respect different opinions, and work together to create a culture where each of us is able to contribute fully. Our unique backgrounds and perspectives strengthen our ability to achieve Amazon's mission of being Earth's most customer-centric company.Amazon’s Account Integrity team within the Customer Trust and Partner Support organization is looking for a passionate, results-oriented Senior Data Scientist to leverage data to drive delivery of projects with huge strategic impact. This team designs and builds high performance systems using machine learning that identify and prevent fraudulent activity and maintain high trust levels with our customers. Fraud prevention is a real-money game where our data science and analytics teams strive to outsmart those who attempt to defraud Amazon and our customers.As a Senior Data Scientist in the Account Integrity group, you will work directly with ML Scientists, Software Development Engineers and Product Managers to monitor the flavor/trend of fraud worldwide and create appropriate solutions to detect and mitigate fraud in a collaborative environment.Successful candidates will have broad expertise in a variety of data science disciplines, including both supervised and unsupervised learning methods, strong analytical skills, be detail oriented, and have excellent problem solving abilities. He/she should also have a demonstrated ability to think strategically and analytically about business, product, and technical challenges, with the ability to work cross-functionally.Roles and Responsibilities:1. Use statistical and machine learning techniques to create scalable detection systems.2. Analyze and extract relevant information from large amounts of Amazon's historical business data to help automate and optimize key processes.3. Design, development and evaluation of highly innovative models for predictive learning.4. Work closely with software engineering and ML teams to drive real-time model implementations and new feature creations.5. Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation.6. Play the role of tech lead on the data science team, mentor fellow scientists.