The breadth of Amazon's computer vision research is on display at ECCV

Research topics range from visual anomaly detection to road network extraction, regression-constrained neural-architecture search to self-supervised learning for video representations.

Amazon's contributions to this year's European Conference on Computer Vision (ECCV) reflect the diversity of the company's research interests. Below is a quick guide to the topics and methods of a dozen ECCV papers whose authors include Amazon scientists.

Fine-grained fashion representation learning by online deep clustering
Yang (Andrew) Jiao, Ning Xie, Yan Gao, Chien-Chih Wang, Yi Sun

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Fashions are characterized by both global attributes, such as “skirt length”, and local attributes, such as “neckline style”. Accurate representations of such attributes are essential to tasks like fashion retrieval and fashion recommendation, but learning representations of each attribute independently ignores shared visual statistics among the attributes. Instead, the researchers treat representation learning as a multitask learning problem, enforcing cluster-level constraints on global structure. The learned representations improve fashion retrieval by a large margin.

GLASS: Global to local attention for scene-text spotting
Roi Ronen, Shahar Tsiper, Oron Anschel, Inbal Lavi, Amir Markovitz, R. Manmatha

Modern text-spotting models combine text detection and recognition into a single end-to-end framework, in which both tasks often rely on a shared global feature map. Such models, however, struggle to recognize text across scale variations (smaller or larger text) and arbitrary word rotation angles. The researchers propose a novel attention mechanism for text spotting, called GLASS, that fuses together global and local features. The global features are extracted from the shared backbone, while the local features are computed individually on resized, high-resolution word crops with upright orientation. GLASS achieves state-of-the-art results on multiple public benchmarks, and the researchers show that it can be integrated with other text-spotting solutions, improving their performance.

GLASS.png
A novel attention mechanism for text spotting, called GLASS, fuses together global and local features. From "GLASS: Global to local attention for scene-text spotting".

Large scale real-world multi-person tracking
Bing Shuai, Alessandro Bergamo, Uta Buechler, Andrew Berneshawi, Alyssa Boden, Joseph Tighe

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This paper presents a new multi-person tracking dataset — PersonPath22 — which is more than an order of magnitude larger than existing high-quality multi-object tracking datasets. The PersonPath22 dataset is specifically sourced to provide a wide variety of conditions, and its annotations include rich metadata that allows the performance of a tracker to be evaluated along these different dimensions. Its large-scale real-world training and test data enable the community to better understand the performance of multi-person tracking systems in a range of scenarios and conditions.

MaCLR: Motion-aware contrastive Learning of representations for videos
Fanyi Xiao, Joseph Tighe, Davide Modolo

Attempts to use self-supervised learning for video have had some success, but existing approaches don’t make explicit use of motion information derived from the temporal sequence, which is important for supervised action recognition tasks. The researchers propose a self-supervised video representation-learning method that explicitly models motion cues during training. The method, MaCLR, consists of two pathways, visual and motion, connected by a novel cross-modal contrastive objective that enables the motion pathway to guide the visual pathway toward relevant motion cues.

MACLR.png
A frame of video (top left) and three different methods of capturing motion. From "MaCLR: Motion-aware contrastive Learning of representations for videos".

PSS: Progressive sample selection for open-world visual representation learning
Tianyue Cao, Yongxin Wang, Yifan Xing, Tianjun Xiao, Tong He, Zheng Zhang, Hao Zhou, Joseph Tighe

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New end-to-end approach to zero-shot video classification dramatically outperforms predecessors.

In computer vision, open-world representation learning is the challenge of learning representations for categories of images not seen during training. Existing approaches make unrealistic assumptions, such as foreknowledge of the number of categories the unseen images fall into, or the ability to determine in advance which unlabeled training examples fall into unseen categories. The researchers’ novel progressive approach avoids such assumptions, selecting at each iteration unlabeled samples that are highly homogenous but belong to classes that are distant from the current set of known classes. High-quality pseudo-labels generated via clustering over these selected samples then improve the feature generalization iteratively.

Rayleigh EigenDirections (REDs): Nonlinear GAN latent space traversals for multidimensional features
Guha Balakrishnan, Raghudeep Gadde, Aleix Martinez, Pietro Perona

Generative adversarial networks (GANs) can map points in a latent space to images, producing extremely realistic synthetic data. Past attempts to control GANs’ outputs have looked for linear trajectories through the space that correspond, approximately, to continuous variation of a particular image feature. The researchers propose a new method for finding nonlinear trajectories through the space, providing unprecedented control over GANs’ outputs, including the ability to hold specified image features fixed while varying others.

Rethinking few-shot object detection on a multi-domain benchmark
Kibok Lee, Hao Yang, Satyaki Chakraborty, Zhaowei Cai, Gurumurthy Swaminathan, Avinash Ravichandran, Onkar Dabeer

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New “meta-learning” approach improves on the state of the art in “one-shot” learning.

Most existing work on few-shot object detection (FSOD) focuses on settings where both the pretraining and few-shot learning datasets are from similar domains. The researchers propose a Multi-dOmain Few-Shot Object Detection (MoFSOD) benchmark consisting of 10 datasets from a wide range of domains to evaluate FSOD algorithms across a greater variety of applications. They comprehensively analyze the effects of freezing layers, different architectures, and different pretraining datasets on FSOD performance, drawing several surprising conclusions. One of these is that, contrary to prior belief, on a multidomain benchmark, fine-tuning (FT) is a strong baseline for FSOD.

SPot-the-Difference: Self-supervised pre-training for anomaly detection and segmentation
Yang Zou, Jongheon Jeong, Latha Pemula, Dongqing Zhang, Onkar Dabeer

Visual anomaly detection is commonly used in industrial quality inspection. This paper presents a new dataset and a new self-supervised learning method for ImageNet pretraining to improve anomaly detection and segmentation in 1-class and 2-class 5/10/high-shot training setups. The Visual Anomaly (VisA) Dataset consists of 10,821 high-resolution color images (9,621 normal and 1,200 anomalous samples) covering 12 objects in three domains, making it one of the largest industrial anomaly detection datasets to date. The paper also proposes a new self-supervised framework — SPot-the-Difference (SPD) — that can regularize contrastive self-supervised and also supervised pretraining to better handle anomaly detection tasks.

SPD contrastive learning.png
Conventional contrastive learning (left) and the contrastive-learning scheme used in SPD (spot-the-difference) training. From "SPot-the-difference: Self-supervised pre-training for anomaly detection and segmentation".

TD-Road: Top-down road network extraction with holistic graph construction
Yang He, Ravi Garg, Amber Roy Chowdhury

Road network extraction from satellite imagery is essential for constructing rich maps and enabling numerous applications in route planning and navigation. Previous graph-based methods used a bottom-up approach, estimating local information and extending a graph iteratively. This paper, by contrast, proposes a top-down approach that decomposes the problem into two subtasks: key point prediction and connectedness prediction. Unlike previous approaches, the proposed method applies graph structures (i.e., locations of nodes and connections between them) as training supervisions for deep neural networks and directly generates road graph outputs through inference.

TD-road.png
A satellite image (left) and three methods for extracting road networks from it: segmentation, bottom-up-graph-based methods, and a new top-down graph-based method (far right). From "TD-Road: Top-down road network extraction with holistic graph construction."

Towards regression-free neural networks for diverse compute platforms
Rahul Duggal, Hao Zhou, Shuo Yang, Jun Fang, Yuanjun Xiong, Wei Xia

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New approach corrects for cases when average improvements are accompanied by specific regressions.

Commercial machine learning models are constantly being updated, and while an updated model may improve performance on average, it can still regress — i.e., suffer “negative flips” — on particular inputs it used to handle correctly. This paper introduces regression-constrained neural-architecture search (REG-NAS), which consists of two components: (1) a novel architecture constraint that enables a larger model to contain all the weights of a smaller one, thus maximizing weight sharing, and (2) a novel search reward that incorporates both top-1 accuracy and negative flips in the architecture search metric. Relative to the existing state-of-the-art approach, REG-NAS enables 33 – 48% reduction of negative flips.

Unsupervised and semi-supervised bias benchmarking in face recognition
Alexandra Chouldechova, Siqi Deng, Yongxin Wang, Wei Xia, Pietro Perona

This paper introduces semi-supervised performance evaluation for face recognition (SPE-FR), a statistical method for evaluating the performance and algorithmic bias of face verification systems when identity labels are unavailable or incomplete. The method is based on parametric Bayesian modeling of face embedding similarity scores, and it produces point estimates, performance curves, and confidence bands that reflect uncertainty in the estimation procedure. Experiments show that SPE-FR can accurately assess performance on data with no identity labels and confidently reveal demographic biases in system performance.

X-DETR: A versatile architecture for instance-wise vision-language tasks
Zhaowei Cai, Gukyeong Kwon, Avinash Ravichandran, Erhan Bas, Zhuowen Tu, Rahul Bhotika, Stefano Soatto

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This paper addresses the challenge of instance-wise vision-language tasks, which require free-form language to align with objects inside an image, rather than the image itself. The paper presents the X-DETR model, whose architecture has three major components: an object detector, a language encoder, and a vision-language alignment module. The vision and language streams are independent until the end, and they are aligned using an efficient dot-product operation. This simple architecture shows good accuracy and fast speeds for multiple instance-wise vision-language tasks, such as open-vocabulary object detection.

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X-DETR addresses the challenge of instance-wise vision-language tasks, which require free-form language to align with objects inside an image, rather than the image itself. From "X-DETR: A versatile architecture for instance-wise vision-language tasks".

Research areas

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US, CA, Palo Alto
We’re working to improve shopping on Amazon using the conversational capabilities of large language models, and are searching for pioneers who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry. You’ll be working with talented scientists, engineers, and technical program managers (TPM) to innovate on behalf of our customers. If you’re fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey! We are open to hiring candidates to work out of one of the following locations: Palo Alto, CA, USA
US, WA, Redmond
Project Kuiper is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. We are searching for talented candidates with experience in spaceflight trajectory modeling and simulation, orbit mechanics, and launch vehicle mission planning. Key job responsibilities This position requires experience in simulation and analysis of astrodynamics models and spaceflight trajectories. This position requires experience in software development for astrodynamics applications and expertise in supporting mission workflow for satellite operations. Strong analysis skills are required to develop engineering studies of complex large-scale dynamical systems. This position requires demonstrated expertise in computational analysis automation and tool development. Working with the Kuiper engineering team, you will: - Develop modeling techniques for analysis and simulation of deployment dynamics of multiple satellites - Support Project Kuiper’s Launch Vehicle Mission Management team with technical expertise in Launch Vehicle trajectory requirements specification - Develop tools to support Mission Management planning for over 80 launches! - Work collaboratively with launch vehicle system technical teams - Provide support of algorithm development and testing for the Kuiper Flight Dynamics System. - Provide software development support of production code. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. We are open to hiring candidates to work out of one of the following locations: Redmond, WA, USA
CN, 11, Beijing
Are you interested in applying your strong quantitative analysis and big data skills to world-changing problems? Are you interested in driving the development of methods, models and systems for strategy planning, transportation and fulfillment network? Are you interested to cooperate with Amazonians around the world? If so, then this is the job for you. Our team, ATE(Analytics Technology and Engineering) is looking for an Applied Scientist to join our growing Science Team in Bangalore (India)/ Beijing(China). We are responsible for creating core analytics tech capabilities, quantative models, platforms development, and data engineering. We develop scalable analytics applications and research models to optimize operations processes. We standardize and optimize data sources and visualization efforts across geographies, build up, and maintain the online business intelligence services and data mart. You will work with other scientists, professional data engineers, business intelligence engineers, and product managers using rigorous quantitative approaches to ensure high quality data tech products for our customers around the world, including India, Australia, Brazil, Mexico, Singapore and Middle East. Amazon is growing rapidly and because we are driven by faster delivery to customers, a more efficient supply chain network, and lower cost of operations, our main focus is in the development of strategic models and automation tools fed by our massive amounts of available data. You will be responsible for building these models/tools that improve the economics of Amazon’s worldwide fulfillment networks in different countries as Amazon increases the speed and decreases the cost to deliver products to customers. You will work on large-scale vehicle routing and scheduling problems under complex operational and physical constraints. You will also identify and evaluate opportunities to reduce variable costs by improving fulfillment center processes, transportation operations and scheduling, and the execution of operational plans. Finally, you will help create the metrics to quantify improvements to the fulfillment costs (e.g., transportation and labor costs) resulting from the application of these optimization models and tools. Key job responsibilities - Design and develop complex mathematical, simulation and optimization models and apply them to define strategic and tactical needs and drive the appropriate business and technical solutions in the areas of vehicle routing, inventory management, network flow, supply chain optimization, demand planning. - Apply theories of mathematical optimization, including linear programming, combinatorial optimization, integer programming, dynamic programming, network flows and algorithms to design optimal or near optimal solution methodologies to be used by in-house decision support tools and software. - Translating business questions and concerns into specific analytical questions that can be answered with available data using Statistical and Machine Learning methods. - Prototype models by using modeling and programming languages with efficient data querying and modeling infrastructure. - Communicate proposals and results in a clear manner backed by data and coupled with actionable conclusions to drive business decisions. - Collaborate with colleagues from multidisciplinary science, engineering and business backgrounds. - Manage your own process. Prioritize and execute on high impact projects, triage external requests, and ensure to deliver projects in time. We are open to hiring candidates to work out of one of the following locations: Beijing, 11, CHN
GB, Cambridge
Our team builds generative AI solutions that will produce some of the future’s most influential voices in media and art. We develop cutting-edge technologies with Amazon Studios, the provider of original content for Prime Video, with Amazon Game Studios and Alexa, the ground-breaking service that powers the audio for Echo. Do you want to be part of the team developing the future technology that impacts the customer experience of ground-breaking products? Then come join us and make history. We are looking for a passionate, talented, and inventive Applied Scientist with a background in Machine Learning to help build industry-leading Speech, Language, Audio and Video technology. As an Applied Scientist at Amazon you will work with talented peers to develop novel algorithms and generative AI models to drive the state of the art in audio (and vocal arts) generation. Position Responsibilities: * Participate in the design, development, evaluation, deployment and updating of data-driven models for digital vocal arts applications. * Participate in research activities including the application and evaluation and digital vocal and video arts techniques for novel applications. * Research and implement novel ML and statistical approaches to add value to the business. * Mentor junior engineers and scientists. We are open to hiring candidates to work out of one of the following locations: Cambridge, GBR | London, GBR
GB, Cambridge
Our team undertakes research together with multiple organizations to advance the state-of-the-art in speech technologies. We not only work on giving Alexa, the ground-breaking service that powers Echo, her voice, but we also develop cutting-edge technologies with Amazon Studios, the provider of original content for Prime Video. Do you want to be part of the team developing the latest technology that impacts the customer experience of ground-breaking products? Then come join us and make history. We are looking for a passionate, talented, and inventive Senior Applied Scientist with a background in Machine Learning to help build industry-leading Speech, Language and Video technology. As a Senior Applied Scientist at Amazon you will work with talented peers to develop novel algorithms and modelling techniques to drive the state of the art in speech and vocal arts synthesis. Position Responsibilities: * Participate in the design, development, evaluation, deployment and updating of data-driven models for digital vocal arts applications. * Participate in research activities including the application and evaluation and digital vocal and video arts techniques for novel applications. * Research and implement novel ML and statistical approaches to add value to the business. * Mentor junior engineers and scientists. We are open to hiring candidates to work out of one of the following locations: Cambridge, GBR | London, GBR
US, VA, Arlington
The GenAI Innovation Center helps AWS customers accelerate their use of Generative AI to solve business challenges and promote innovation across their organizations. The Public Sector team focuses on public sector customers and their unique challenges. As a data scientist, you have deep and broad experience as an ML practitioner. You interface directly with customers to understand and identify their challenges that can be addressed by Generative AI. You build secure solutions that can scale to the size of the problem at hand and guide customers through your rigorous evaluation process. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. You're part of both a small team dedicated to public sector customers and a global organization enabling customers to accelerate their progress on GenAI. This position requires that the candidate selected be a US Citizen. AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. Key job responsibilities The primary responsibilities of this role are to: Design, develop, and evaluate innovative ML models to solve diverse challenges and opportunities across industries. Interact with customer directly to understand their business problems, and help them with defining and implementing scalable Generative AI solutions to solve them. Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new solution. A day in the life 1. Team with a GenAI strategist to understand a customer problem and provide guidance on how and whether GenAI can help address the issue. 2. Share your latest experiment results or challenges with other scientists on the team. 3. Collaborate on a blog post to share the results and methods used in your most recent customer success. 4. Attend or a deliver a tech talk to highlight a project you or a team mate just completed. 5. Provide feedback to your team during a code review. 6. Meet with customer stakeholders to demonstrate the latest progress on their problem. About the team Diverse Experiences 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 flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Denver, CO, USA
US, CA, Santa Clara
Amazon is looking for a motivated individual with strong analytical and algorithmic skills and practical experience to join the Modeling and Optimization (MOP) Routing Science team. Your main focus will be on developing and improving our last-mile experience, with emphasis on algorithmic and analytical work. We are looking for candidates with proven ability to design, implement, and evaluate state-of-the-art solutions to large-scale optimization problems, working closely with software development engineers. The position requires strong background in combinatorial optimization, algorithms, algorithm engineering, and data structures, particularly as it applies to vehicle routing and related problems. Familiarity with data science and Machine Learning techniques is a plus. You will also play an integral role in the network planning, modeling, and analysis that will improve the efficiency and cost effectiveness of global fulfillment operations. You will identify and evaluate opportunities to reduce variable costs by improving the transportation network topology, inventory placement, transportation operations and scheduling, fulfillment center processes, and the execution to operational plans. You will also improve the efficiency of capital investment by helping plan the location and deployment of fixed assets. Finally, you will help create the metrics to quantify improvements to the fulfillment costs (e.g., transportation and labor costs) resulting from the application of these optimization models and tools. Key job responsibilities We are looking for candidates with proven ability to design, implement, and evaluate state-of-the-art solutions to large-scale optimization problems, working closely with software development engineers. The position requires strong background in combinatorial optimization, algorithms, algorithm engineering, and data structures, particularly as it applies to vehicle routing problems. We are open to hiring candidates to work out of one of the following locations: Santa Clara, CA, USA
US, WA, Seattle
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python is necessary, and experience with SQL and UNIX would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Seattle
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python is necessary, and experience with SQL and UNIX would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, MA, North Reading
We are looking for experienced scientists and engineers to explore new ideas, invent new approaches, and develop new solutions in the areas of Controls, Dynamic modeling and System identification. 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. Key job responsibilities Applied Scientists take on big unanswered questions and guide development team to state-of-the-art solutions. We want to hear from you if you have deep industry experience in the Mechatronics domain and : * 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 found We are open to hiring candidates to work out of one of the following locations: North Reading, MA, USA | Westborough, MA, USA