Prime Video's work on 3-D scene reconstruction, image representation

CVPR papers examine the recovery of 3-D information from camera movement and learning general representations from weakly annotated data.

At this year’s Conference on Computer Vision and Pattern Recognition (CVPR), Prime Video is presenting a pair of papers that indicate the range of problems we work on.

In one paper, “Depth-guided sparse structure-from-motion for movies and TV shows”, we present a method for determining the camera movement and 3-D geometry of scenes depicted in videos. An important application of this work is to enable the accurate insertion of digital objects into already recorded videos. Our approach, which leverages off-the-shelf depth estimators to enhance the standard geometric-optimization approach, results in improvements of 10% to 30% on six different performance measures, relative to the best-performing prior technique.

SfM.gif
The Prime Video structure-from-motion system at work. At top is the input video. At lower left is the video with keypoints (colored circles) added. The keypoints are tracked accurately from frame to frame, and their color indicates their depth, as estimated by a machine learning model. At lower right is the 3-D model of the keypoints (whose rotation, to demonstrate the 3-D structure, is not synchronized with the video).

In the other paper, “Robust cross-modal representation learning with progressive self-distillation,” we expand on the CLIP method of using paired images and texts found online to train a model that produces image and text representations useful for downstream tasks, such as image classification or text-based image retrieval.

Where CLIP enforces a hard alignment between Web-crawled images and their associated texts, our method is more flexible, allowing for partial correspondences between a given image and texts associated with other images. We also use a self-distillation technique, in which our model progressively creates some of its own training targets, to steadily refine its representations.

Related content
Detectors for block corruption, audio artifacts, and errors in audio-video synchronization are just three of Prime Video’s quality assurance tools.

In two different image classification settings, our method outperforms CLIP across the board, by significant margins — 30% to 90% — on some datasets. Our method also consistently outperforms its CLIP counterpart on the tasks of image-based text retrieval and text-based image retrieval.

Structure-from-motion

Structure-from-motion is the problem of determining the 3-D structure of a scene from parallax — the relative displacement of objects in the scene as the camera moves. There are robust solutions for videos with large camera movements, but they don’t work as well for feature films and TV shows, where the camera movements tend to be more restrained.

The standard approach to determining structure from motion uses geometric optimization. First, the method estimates the location of a set of 3-D points in the scene, and then, based on that estimation, it re-projects them onto a 2-D image corresponding to each camera location. The optimization procedure minimizes the distance between points in the original 2-D image and the corresponding points of the 2-D projection.

We improve on this approach by introducing depth estimates performed by off-the-shelf, pretrained models. Instead of minimizing only the difference between the original and the projected 2-D points, our approach minimizes both the reprojection error of the 2-D points and the depth measurement error, relative to the output of the depth estimation model.

Double loss.png
Our approach jointly minimizes 2-D reprojection error and depth estimate error.

Our approach begins by using a standard method to detect image keypoints — salient points in the image, usually at object corners and other edge intersections — and identify their correspondences across successive frames of video. Then, through bilinear interpolation, we use the depth map obtained from an off-the-shelf depth estimator to determine the ground-truth keypoint depths. We use the depth information not only during optimization but also during the initialization stage of the process, when we produce our initial estimates of 3-D scene structure and relative camera pose.

SfM.png
The Prime Video structure-from-motion technique identifies keypoints in input video, finds their correspondences across frames, and then estimates their depth using bilinear interpolation on a dense depth map.

We experimented with several different depth estimation models and found that the results of our approach were essentially the same with all of them. And, in all cases, our approach improved substantially on the state of the art.

Cross-modal representations

In natural-language processing, the best-performing models in recent years have been built on top of language models that learn generic linguistic representations from huge corpora of unannotated public texts. The language models can then be fine-tuned for specific tasks with minimal additional data.

CLIP (contrastive language-image pretraining) seeks to do something similar for computer vision, learning generic visual representations from images harvested from the Web and their associated texts.

Related content
The switch to WebAssembly increases stability, speed.

Like many such weakly supervised models, CLIP is trained through contrastive learning. Intuitively, for each training image, the model is fed two texts: one, the positive training example, is the text associated with the image online; the other text, the negative example, is randomly chosen. CLIP learns a data representation that pulls the image and the positive text together in the representation space and pushes the image and the negative text apart.

Although CLIP has yielded impressive results on downstream computer vision tasks, its training approach has two drawbacks. First, the web-harvested data is noisy: the text associated with an image may in fact be semantically unrelated to it. Conversely, the text randomly selected as a negative example may in fact be semantically related to the image. CLIP can thus steer the model toward erroneous associations and away from correct ones.

Our method attempts to address this problem. Rather than learn a hard alignment between image and text, we learn a soft alignment, which gives the resulting model more interpretive flexibility.

For example, in one of our experiments, both the CLIP baseline and our model were trained on datasets that included images of goldfish. When presented with an image of a stained-glass window depicting a goldfish — a type of image not included in the training data — CLIP guessed that it was a guinea pig or maybe a beer glass, while our model guessed that it was a goldfish or possibly a clown fish. That is, our model learned a representation general enough to accommodate the stylization of the stained-glass artist’s rendering style.

CV model learning.png
CLIP’s contrastive-learning procedure enforces connections between web-harvested images and their associated texts (green lines, at left) while dissociating them from other images’ texts (red lines). Our approach instead privileges associated texts but also learns softer, probabilistic alignments with other images’ texts (dotted blue lines).

Our model learns its soft alignments through a self-distillation process. First, the model learns an initial data representation through the same contrastive-loss function that CLIP uses.

Over the course of training, however, we use the model itself to make predictions about the training examples and use those predictions as additional training targets. At first, the loss function gives these self-predictions little weight, but it gradually increases the weight as training progresses.

Related content
In a pilot study, an automated code checker found about 100 possible errors, 80% of which turned out to require correction.

The idea is that, over time, the model learns more reliable correlations between training images and texts. Self-distillation reinforces those correlations, so the model isn’t encouraged to break semantic connections between images and texts that may very well be present in the data. Similarly, over time, the model learns to give less weight to spurious connections between images and the texts initially associated with them.

The great virtue of general representation models like ours and CLIP is that they can be applied to a wide variety of computer vision problems. So the accuracy improvements that our approach affords should pay dividends for Prime Video customers in a range of contexts over the next few years.

Research areas

Related content

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. Amazon Robotics is seeking Applied Science Interns and Co-ops with a passion for robotic research to work on cutting edge algorithms for robotics. Our team works on challenging and high-impact projects within robotics. Examples of projects include allocating resources to complete a million orders a day, coordinating the motion of thousands of robots, autonomous navigation in warehouses, identifying objects and damage, and learning how to grasp all the products Amazon sells. As an Applied Science Intern/Co-op at Amazon Robotics, you will be working on one or more of our robotic technologies such as autonomous mobile robots, robot manipulators, and computer vision identification technologies. The intern/co-op project(s) and the internship/co-op location are determined by the team the student will be working on. Please note that by applying to this role you would be considered for Applied Scientist summer intern, spring co-op, and fall co-op roles on various Amazon Robotics teams. These teams work on robotics research within areas such as computer vision, machine learning, robotic manipulation, navigation, path planning, perception, optimization and more. Learn more about Amazon Robotics: https://amazon.jobs/en/teams/amazon-robotics We are open to hiring candidates to work out of one of the following locations: North Reading, MA, USA | Seattle, WA, USA | Westborough, MA, USA
CA, BC, Vancouver
Amazon Web Services (AWS) is building a world-class marketing organization that drives awareness and customer engagement with the goal of educating developers, IT and line-of-business professionals, startups, partners, and executive decision makers about AWS services and solutions, their benefits, and differentiation. As the central data and science organization in AWS Marketing, the Data: Science and Engineering (D:SE) team builds measurement products, AI/ML models for targeting, and self-service insights capabilities for AWS Marketing to drive better measurement and personalization, improve data access and analytical self-service, and empower strategic data-driven decisions. We work globally as a central team and establish standards, benchmarks, and best practices for use throughout AWS Marketing. We are looking for a Principal Data Scientist with deep expertise in scaling measurement science, content ranking and rapid experimentation at scale, with strong interest in building scalable solutions in partnership with our engineering organization. You will lead strategic measurement science initiatives across AWS Marketing & Sales ranging anywhere between recommender engines, scaling experimentation and measurement science, real-time inference, and cross-channel orchestration. You are an hands-on innovator who can contribute to advancing Marketing measurement technology in a B2B environment, and push the limits on what’s scientifically possible with a razor sharp focus on measurable customer and business impact. You will work with recognized B2B Marketing Science and AI/ML experts to develop large-scale, high-performing measurement science models and AI/ML capabilities. We are at a pivotal moment in our organization where AI/ML and measurement velocity has reached an unseen momentum, and we need to scale fast in order to maintain it. Your work will be a key input into a few of our key business goals. You will advance the state of the art in measurement at scale. We are open to hiring candidates to work out of one of the following locations: Vancouver, BC, CAN
US, VA, Herndon
Do you love decomposing problems to develop machine learning (ML) products that impact millions of people around the world? Would you enjoy identifying, defining, and building ML software solutions that revolutionize how businesses operate? The Global Practice Organization in Professional Services at Amazon Web Services (AWS) is looking for a Software Development Engineer II to build, deliver, and maintain complex ML products that delight our customers and raise our performance bar. You’ll design fault-tolerant systems that run at massive scale as we continue to innovate best-in-class services and applications in the AWS Cloud. Key job responsibilities Our ML Engineers collaborate across diverse teams, projects, and environments to have a firsthand impact on our global customer base. You’ll bring a passion for the intersection of software development with generative AI and machine learning. You’ll also: - Solve complex technical problems, often ones not solved before, at every layer of the stack. - Design, implement, test, deploy and maintain innovative ML solutions to transform service performance, durability, cost, and security. - Build high-quality, highly available, always-on products. - Research implementations that deliver the best possible experiences for customers. A day in the life As you design and code solutions to help our team drive efficiencies in ML architecture, you’ll create metrics, implement automation and other improvements, and resolve the root cause of software defects. You’ll also: - Build high-impact ML solutions to deliver to our large customer base. - Participate in design discussions, code review, and communicate with internal and external stakeholders. - Work cross-functionally to help drive business solutions with your technical input. - Work in a startup-like development environment, where you’re always working on the most important stuff. About the team The Global Practice Organization for Analytics is a team inside the AWS Professional Services Organization. Our mission in the Global Practice Organization is to be at the forefront of defining machine learning domain strategy, and ensuring the scale of Professional Services' delivery. We define strategic initiatives, provide domain expertise, and oversee the development of high-quality, repeatable offerings that accelerate customer outcomes. Inclusive Team Culture Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 85,000 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. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Work/Life Balance Our team puts a high value on work-life harmony. Striking a healthy balance between your personal and professional life is crucial to your happiness and success here. We are a customer-obsessed organization—leaders start with the customer and work backwards. They work vigorously to earn and keep customer trust. As such, this is a customer facing role in a hybrid delivery model. Project engagements include remote delivery methods and onsite engagement that will include travel to customer locations as needed. Mentorship & Career Growth Our 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. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded professional and enable them to take on more complex tasks in the future. This is a customer-facing role and you will be required to travel to client locations and deliver professional services as needed. We are open to hiring candidates to work out of one of the following locations: Atlanta, GA, USA | Austin, TX, USA | Boston, MA, USA | Chicago, IL, USA | Herndon, VA, USA | Minneapolis, MN, USA | New York, NC, USA | San Diego, CA, USA | San Francisco, CA, USA | Seattle, WA, USA
US, WA, Seattle
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best. Key job responsibilities • Develop automated laboratory workflows. • Perform data QC, document results, and communicate to stakeholders. • Maintain updated understanding and knowledge of methods. • Identify and escalate equipment malfunctions; troubleshoot common errors. • Participate in the updating of protocols and database to accurately reflect the current practices. • Maintain equipment and instruments in good operating condition • Adapt to unexpected schedule changes and respond to emergency situations, as needed. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Seattle
Are you excited about developing generative AI and foundation models to revolutionize automation, robotics and computer vision? Are you looking for opportunities to build and deploy them on real problems at truly vast scale? At Amazon Fulfillment Technologies and Robotics we are on a mission to build high-performance autonomous systems that perceive and act to further improve our world-class customer experience - at Amazon scale. We are looking for scientists, engineers and program managers for a variety of roles. The Amazon Robotics software team is seeking a Applied Scientist to focus on large vision and manipulation machine learning models. This includes building multi-viewpoint and time-series computer vision systems. It includes using machine learning to drive hardware movement. It includes building large-scale models using data from many different tasks and scenes. This work spans from basic research such as cross domain training, to experimenting on prototype in the lab, to running wide-scale A/B tests on robots in our facilities. Key job responsibilities * Research vision - Where should we be focusing our efforts * Research delivery – Proving/dis-proving strategies in offline data or in the lab * Production studies - Insights from production data or ad-hoc experimentation. About the team This team invents and runs robots focused on grasping and packing items. These are typically 6-dof style robotic arms. Our work ranges from the long-term-research on basic science to deploying/supporting large production fleets handling billions of items per year. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, VA, Arlington
Amazon launched the Generative AI (GenAI) Innovation Center (GAIIC) in Jun 2023 to help AWS customers accelerate enterprise innovation and success with Generative AI (https://press.aboutamazon.com/2023/6/aws-announces-generative-ai-innovation-center). Customers such as Highspot, Lonely Planet, Ryanair, and Twilio are engaging with the GAI Innovation Center to explore developing generative solutions. GAIIC provides opportunities to innovate in a fast-paced organization that contributes to game-changing projects and technologies that get deployed on devices and in the cloud. As a data scientist at GAIIC, you are proficient in designing and developing advanced Generative AI based solutions to solve diverse customer problems. You will be working with terabytes of text, images, and other types of data to solve real-world problems through Gen AI. You will be working closely with account teams and ML strategists to define the use case, and with other scientists and ML engineers on the team to design experiments, and find new ways to deliver value to the customer. The successful candidate will possess both technical and customer-facing skills that will allow you to be the technical “face” of AWS within our solution providers’ ecosystem/environment as well as directly to end customers. You will be able to drive discussions with senior technical and management personnel within customers and partners. This position requires that the candidate selected be a US Citizen and currently possess and maintain an active Top Secret security clearance. About the team Work/Life Balance Our 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 Growth Our 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. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Denver, CO, USA
US, VA, Arlington
Amazon’s mission is to be the most customer centric company in the world. The Workforce Staffing (WFS) organization is on the front line of that mission by hiring the hourly fulfillment associates who make that mission a reality. To drive the necessary growth and continued scale of Amazon’s associate needs within a constrained employment environment, Amazon has created the Workforce Intelligence (WFI) team. This team will (re)invent how Amazon attracts, communicates with, and ultimately hires its hourly associates. This team owns multi-layered research and program implementation to drive deep learning, process improvements, and strategic recommendations to global leadership. Are you passionate about data? Do you enjoy questioning the status quo? Do complex and difficult challenges excite you? If yes, this may be the team for you. The Data Scientist will be responsible for creating cutting edge algorithms, predictive and prescriptive models as well as required data models to facilitate WFS at-scale warehouse associate hiring. This role acts as an internal consultant to the marketing, biz ops and candidate experience teams covering responsibilities such as at-scale hiring process improvement, analyzing large scale candidate/associate data and being strategic to providing best candidate hiring experience to WFS warehouse associate candidates. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA
US, WA, Seattle
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, CA, Sunnyvale
Are you passionate about solving unique customer-facing problem at Amazon scale? Are you excited by developing and productionizing machine learning, deep learning algorithms and leveraging tons of Amazon data to learn and infer customer shopping patterns? Do you enjoy working with a diverse set of engineers, machine learning scientists, product managers and user-experience designers? If so, you have found the right match! Virtual Try On (VTO) at Amazon Fashion & Fitness is looking for an exceptional Applied Scientist to join us to build our next generation virtual try on experience. Our goal is to help customers evaluate how products will fit and flatter their unique self before they ship, transforming customers' shopping into a personalized journey of inspiration, discovery, and evaluation. In this role, you will be responsible for building scalable computer vision and machine learning (CVML) models, and automating their application and expansion to power customer-facing features. Key job responsibilities - Tackle ambiguous problems in Computer Vision and Machine Learning, and drive full life-cycle of CV/ML projects. - Build Computer Vision, Machine Learning and Generative AI models, perform proof-of-concept, experiment, optimize, and deploy your models into production. - Investigate and solve exciting and difficult challenges in Image Generation, 3D Computer Vision, Generative AI, Image Understanding and Deep Learning. - Run A/B experiments, gather data, and perform statistical tests. - Lead development and productionalization of CV, ML, and Gen AI models and algorithms by working across teams. Deliver end to end. - Act as a mentor to other scientists on the team. We are open to hiring candidates to work out of one of the following locations: Sunnyvale, CA, USA
US, CA, Sunnyvale
Are you passionate about solving unique customer-facing problem in the Amazon scale? Are you excited by developing and productizing machine learning, deep learning algorithms and leverage tons of Amazon data to learn and infer customer shopping patterns? Do you enjoy working with a diversity of engineers, machine learning scientists, product managers and user-experience designers? If so, you have found the right match! Fashion is extremely fast-moving, visual, subjective, and it presents numerous unique problem domains such as product recommendations, product discovery and evaluation. The vision for Amazon Fashion is to make Amazon the number one online shopping destination for Fashion customers by providing large selections, inspiring and accurate recommendations and customer experience. The mission of Fit science team as part of Fashion Tech is to innovate and develop scalable ML solutions to provide personalized fit and size recommendation when Amazon Fashion customers evaluate apparels or shoes online. The team is hiring Applied Scientist who has a solid background in applied Machine Learning and a proven record of solving customer-facing problems via scalable ML solutions, and is motivated to grow professionally as an ML scientist. Key job responsibilities - Tackle ambiguous problems in Machine Learning and drive full life-cycle Machine Learning projects. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production. - Run A/B experiments, gather data, and perform statistical tests. - Establish scalable, efficient, automated processes for large-scale data mining, machine-learning model development, model validation and serving. - Work closely with software engineers and product managers to assist in productizing your ML models. We are open to hiring candidates to work out of one of the following locations: Sunnyvale, CA, USA