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.

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.

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.

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.

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, MA, North Reading
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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 or R 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 We are open to hiring candidates to work out of one of the following locations: London, GBR
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LU, Luxembourg
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US, CA, Santa Clara
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IN, KA, Bengaluru
Job Description ATE (Analytics, Technology and Engineering) is a multi-disciplinary team of scientists, engineers, and technicians, all working to innovate in operations for the benefit of our customers. Our team is responsible for creating core analytics, science capabilities, platforms development and data engineering. We develop scalable analytics applications and research modeling to optimize operation processes.. You will work with professional software development managers, data engineers, data scientists, applied scientists, 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. We are on the lookout for an enthusiastic and highly analytical individual to be a part of our journey. 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 emerging countries as Amazon increases the speed and decreases the cost to deliver products to customers. You will identify and evaluate opportunities to reduce variable costs by improving fulfillment center processes, transportation operations and scheduling, and the execution to operational plans. You will also improve the efficiency of capital investment by helping the fulfillment centers to improve storage utilization and the effective use of automation. 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. Major responsibilities include: · In this role, you will be responsible for developing and implementing innovative, scalable models and tools aimed at tackling novel challenges within Amazon’s global fulfillment network. Collaborating with fellow scientists from various teams, you will work on integrated solutions to enhance fulfillment speed, reduce costs. Your in-depth comprehension of business challenges will enable you to provide scientific analyses that underpin critical business decisions, utilizing a diverse range of methodologies. You’ll have the opportunity to design scientific tool platforms, deploy models, create efficient data pipelines, and streamline existing processes. Join us in shaping the future of Amazon’s global retail business by optimizing delivery speed at scale and making a lasting impact on the world of e-commerce. If you’re passionate about solving complex problems and driving innovation, we encourage you to apply. About the team This team is responsible for applying science based algo and techniques to solve the problems in operation and supply chain. Some of these problems include, volume forecasting, capacity planning, fraud detection, scenario simulation and using LLM/GenAI for process efficiency We are open to hiring candidates to work out of one of the following locations: Bengaluru, KA, IND
IL, Tel Aviv
Are you passionate about pushing the boundaries of computer vision, generative AI, deep learning, and machine learning? Ready to tackle challenges in document understanding at scale? We’re looking for innovative minds to join our world-class team at AWS, where you’ll collaborate with leading researchers, academics, and engineers on Amazon Textract. Why AWS? Be part of the leading cloud service provider powering innovation and positive impact. Work on real-world problems alongside tech and business giants. Access to unlimited data and computational resources. Collaborate with world-class researchers and developers. Deploy solutions at AWS scale and publish your work at top conferences. Focus Areas: - LLMs, document understanding, scene text recognition. - Visual question answering, NLP+vision, layout understanding. Locations: Tel Aviv and Haifa Think you’re a fit? Dive into the world of AWS Computer Vision and help us innovate at the forefront of technology. Key job responsibilities - Design cutting-edge neural network architectures. - Create document understanding solutions for complex scenarios and large visual datasets. - Set benchmarks and success criteria for model performance. - Collaborate across AWS and Amazon to bring scientific breakthroughs to our customers. - Add your unique creativity to our multidisciplinary team. - Mentor junior scientists and interns/PhD students. We are open to hiring candidates to work out of one of the following locations: Haifa, ISR | Tel Aviv, ISR
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. As a Applied Scientist at the intersection of machine learning and the life sciences, you will participate in developing exciting products for customers. Our team rewards curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the cutting edge of both academic and applied research in this product area, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with others teams. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Bellevue
As a Principal Research Scientist in the Amazon Artificial General Intelligence (AGI) Data Services organization, you will be responsible for sourcing and quality of massive datasets powering Amazon's AI. You will play a critical role in driving innovation and advancing the state-of-the-art in natural language processing and machine learning. You will be responsible for developing and implementing cutting-edge algorithms and techniques to extract valuable insights from large-scale data sources. You will work closely with cross-functional teams, including product managers, engineers, and data scientists to ensure that our AI systems are aligned with human policies and preferences. Key job responsibilities - Responsible for sourcing and quality of massive datasets powering Amazon's AI. - Collaborate with cross-functional teams to ensure that Amazon’s AI models are aligned with human preferences. - Develop and implement strategies to improve the efficiency and effectiveness of programs delivering massive datasets. - Identify and prioritize research opportunities that have the potential to significantly impact our AI systems. - Communicate research findings and progress to senior leadership and stakeholders. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Boston, MA, 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. 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 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