WACV: Where application-based research finds a home

As video scales up — in both duration and resolution — it raises new research questions.

Zongyi (Joe) Liu is a principal scientist in the Amazon Customer Experience and Business Trends (CXBT) organization, which evaluates the customer experience across Amazon’s products and services and works to improve it. Among the products the organization evaluates are deployed artificial-intelligence models, such as Alexa’s voice recognizer or the computer vision models that identify scene boundaries in Prime Video content.

Joe Liu.jpg
Amazon principal scientist Zongyi (Joe) Liu outside St. Joseph's Oratory in Montréal, Quebec.

Liu is a coauthor on a paper at this year’s Winter Conference on Applications of Computer Vision (WACV), and he’s also one of the organizers of the conference’s Workshop on Video/Audio Quality in Computer Vision.

As its name suggests, Liu says, WACV is distinctive among the major computer vision conferences for its focus on applications. While Amazon researchers publish dozens of papers on fundamental computer vision research each year at conferences like CVPR and ECCV, WACV is an inviting venue for scientific work that directly addresses business problems.

“One thing unique to WACV is that it has an application track, where you don't need to have a fundamental innovation in the algorithm area,” Liu says. “It provides opportunities for industry research scientists to present their work. If you prove you can make an application work, they will take it.”

Expanding vision

Two examples of industrial applications that are driving research in computer vision, Liu says, are long-video segmentation — finding the scene boundaries in a long video — and object recognition and semantic segmentation in ultrahigh-definition (UHD) video.

Related content
Automated methods with a little human guidance use annotators’ time much more efficiently.

“Computer vision is growing both spatially and temporally, and it’s hard in both directions,” Liu says. “Today, people focus on, say, 10-second videos, because that's what a GPU memory can handle. Once you go to an hour — or with a sporting event, three hours — that's almost impossible for any memory. If you break it down to a few seconds, that's too granular. You don't have a full picture.

“And the individual images are fundamentally changing, because we're going to UHD. Most images today are 1,080 [pixels per row] or even 640. But when you go to 2,096, it raises new questions. How do you make the algorithm scalable to do object detection? You can downsample, but that can be problematic if you want to detect a very small object. How do you detect the quality of the image and know if it’s been upscaled or if it’s original? What's the quality of it? How does a human perceive it? These are challenges in terms of both computational power and memory.”

Audio-video alignment

Liu and his colleagues' paper on video segmentation examines the problem of recognizing which segments of a video stream are advertisements.

“The main contribution of my paper is that we are using the audio instead of video, because using the audio to start segmentation is much more scalable,” Liu says. “Our time is around eight to ten times faster than the state of art.”

The same principle — that scene breaks and audio disjunctions should align — can be used in reverse, Liu explains, to identify cases in which audio and video have gotten out of sync.

“Audio-video synchronization is a very hard topic because it’s multimodal and because humans are very sensitive to it,” Liu says. “You can accept that an image becomes lower resolution for some time, but if audio and video get out of sync for just a half-second, it can drive you crazy.

Related content
University of Wisconsin-Madison associate professor and ARA recipient has authored a series of pioneering papers on real-time object instance segmentation.

“One way people identify it is through cross-correlation. They have a window basically shift the audio with your image features and see which one gives the highest correlation. I can give you an example that I published earlier. When one shot ends and the next starts, you can tell. I detect the transition period from both video and audio. If the video and audio are in sync, their transition times should be aligned. There’s some noise, but if you’re systematically out of sync, you should be able to tell by aggregating all those video and audio signals.”

In this and similar ways, Liu says, focusing on the customer experience can give rise to new and interesting research questions. And when those questions involve computer vision, WACV is the ideal place to present them.

“In industry, we have scientists who aren’t working on academic algorithms,” Liu says. “WACV is really a good conference for scientists like us to present our work."

Research areas

Related content

US, WA, Bellevue
Conversational AI ModEling and Learning (CAMEL) team is part of Amazon Devices organization where our mission is to build a best-in-class Conversational AI that is intuitive, intelligent, and responsive, by developing superior Large Language Models (LLM) solutions and services which increase the capabilities built into the model and which enable utilizing thousands of APIs and external knowledge sources to provide the best experience for each request across millions of customers and endpoints. We are looking for a passionate, talented, and resourceful Senior Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), Recommender Systems and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. A successful candidate will have strong machine learning background and a desire to push the envelope in one or more of the above areas. The ideal candidate would also have hands-on experiences in building Generative AI solutions with LLMs, enjoy operating in dynamic environments, be self-motivated to take on challenging problems to deliver big customer impact, moving fast to ship solutions and then iterating on user feedback and interactions. Key job responsibilities As a Senior Applied Scientist, you will leverage your technical expertise and experience to demonstrate leadership in tackling large complex problems, setting the direction and collaborating with other talented applied scientists and engineers to research and develop LLM modeling and engineering techniques to reduce friction and enable natural and contextual conversations. You will analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence. You will work on core LLM technologies, including Prompt Engineering, Model Fine-Tuning, Reinforcement Learning from Human Feedback (RLHF), Evaluation, etc. Your work will directly impact our customers in the form of novel products and services .
US, CA, Pasadena
The Amazon Web Services (AWS) Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians, on a mission to develop a fault-tolerant quantum computer. We are looking to hire a Research Scientist with fabrication and data analysis experience working on all elements of a superconducting circuit. The position is on-site at our lab, located on the in Pasadena, CA. The ideal candidate will have had prior experience building software tools for data analysis and visualization to enable deep diving into fabrication details, electrical test data. We are looking for candidates with strong engineering principles, resourcefulness and data science experience. Organization and communication skills are essential. Key job responsibilities * Develop and automate data pipeline pertinent to superconducting device fabrication. * Develop analytical tools to uncover new information about established and new processes. * Develop new or contribute to modifying existing data visualization tools. * Utilize machine learning to enable better deeper dives into fabrication and related data. * Interface with various software, design, fabrication and electrical test teams to enable new functionalities. A day in the life The role will be vital to the fabrication team and quantum computing device integration mechanism. The candidate will develop software based analytical tools to enable data driven decisions across projects related to fabrication and supporting infrastructure. Each fabrication run delivers additional data. The candidate will stay close to the details of fabrication providing data analysis and quick feedback to key stakeholders. At the end of fabrication runs custom and standardized reports will be generated by the candidate to provide insights into data generated from the run. This position may require occasional weekend work. About the team AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.
US, WA, Seattle
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches
CA, ON, Toronto
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! As an Applied Scientist on this team, you will: - Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. - Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - 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. - Research new and innovative machine learning approaches. - Recruit Applied Scientists to the team and provide mentorship. Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Team video https://youtu.be/zD_6Lzw8raE
US, WA, Seattle
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! As the Data Science Manager on this team, you will: - Lead of team of scientists, business intelligence engineers, etc., on solving science problems with a high degree of complexity and ambiguity. - Develop science roadmaps, run annual planning, and foster cross-team collaboration to execute complex projects. - Perform hands-on data analysis, build machine-learning models, run regular A/B tests, and communicate the impact to senior management. - Hire and develop top talent, provide technical and career development guidance to scientists and engineers in the organization. - Analyze historical data to identify trends and support optimal decision making. - Apply statistical and machine learning knowledge to specific business problems and data. - Formalize assumptions about how our systems should work, create statistical definitions of outliers, and develop methods to systematically identify outliers. Work out why such examples are outliers and define if any actions needed. - Given anecdotes about anomalies or generate automatic scripts to define anomalies, deep dive to explain why they happen, and identify fixes. - Build decision-making models and propose effective solutions for the business problems you define. - Conduct written and verbal presentations to share insights to audiences of varying levels of technical sophistication. Why you will love this opportunity: Amazon has invested heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Team video ~ https://youtu.be/zD_6Lzw8raE
US, WA, Seattle
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! As an Applied Science Manager in Machine Learning, you will: - Directly manage and lead a cross-functional team of Applied Scientists, Data Scientists, Economists, and Business Intelligence Engineers. - Develop and manage a research agenda that balances short term deliverables with measurable business impact as well as long term investments. - Lead marketplace design and development based on economic theory and data analysis. - Provide technical and scientific guidance to team members. - Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative and business judgment - Advance the team's engineering craftsmanship and drive continued scientific innovation as a thought leader and practitioner. - Develop science and engineering roadmaps, run annual planning, and foster cross-team collaboration to execute complex projects. - Perform hands-on data analysis, build machine-learning models, run regular A/B tests, and communicate the impact to senior management. - Collaborate with business and software teams across Amazon Ads. - Stay up to date with recent scientific publications relevant to the team. - Hire and develop top talent, provide technical and career development guidance to scientists and engineers within and across the organization. Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Team video ~ https://youtu.be/zD_6Lzw8raE
US, WA, Seattle
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches
US, CA, San Diego
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches
US, CA, San Diego
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches
US, WA, Seattle
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches