Welcome To The

Stanford Vision and Learning Lab

The Stanford Vision and Learning Lab (SVL) at Stanford is directed by Professors Fei-Fei Li, Juan Carlos Niebles, and Silvio Savarese. We are tackling fundamental open problems in computer vision research and are intrigued by visual functionalities that give rise to semantically meaningful interpretations of the visual world.

SVL Group Image 2018
JackRabbot

JackRabbot

Our work at the SVL is making practical a new generation of autonomous agents that can operate safely alongside humans in dynamic crowded environments such as terminals, malls, or campuses. The Stanford “Jackrabbot”, which takes it name from the nimble yet shy Jackrabbit, is a self-navigating automated electric delivery cart capable of carrying small payloads.

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PAIR

Stanford People, AI & Robots Group (PAIR)

Stanford People, AI & Robots Group (PAIR) is a research group under the Stanford Vision & Learning Lab that focuses on developing methods and mechanisms for generalizable robot perception and control. We work on challenging open problems at the intersection of computer vision, machine learning, and robotics. We develop algorithms and systems that unify in reinforcement learning, control theoretic modeling, and 2D/3D visual scene understanding to teach robots to perceive and to interact with the physical world.

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ActivityNet

ActivityNet: A Large-Scale Video Benchmark for Human Activity Understanding

Our work at the SVL is making practical a new generation of autonomous agents that can operate safely alongside humans in dynamic crowded environments such as terminals, malls, or campuses. The Stanford “Jackrabbot”, which takes it name from the nimble yet shy Jackrabbit, is a self-navigating automated electric delivery cart capable of carrying small payloads.

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AI Assisted Care

ActivityNet: A Large-Scale Video Benchmark for Human Activity Understanding.

The Partnership in AI-Assisted Care (PAC) is an interdisciplinary collaboration between the School of Medicine and the Computer Science department focusing on cutting edge computer vision and machine learning technologies to solve some of healthcare's most important problems.

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Visual Genome

Visual Genome

To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in an image. Visual Genome is a new dataset to connect dense, structured image concepts to language.

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About SVL

Our research addresses the theoretical foundations and practical applications of computational vision. We are focused on discovering and proposing the fundamental principles, algorithms and implementations for solving high-level visual perception and cognition problems involving computational geometry, automated image and video analysis, and visual reasoning. At the same time, our curiosity leads us to study the underlying neural mechanisms that enable the human visual system to perform high level visual tasks with amazing speed and efficiency.

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Portfolio Image
Portfolio Image

Press Coverage

SVL Alumnus hired as Vice President of AI at Tesla. “Tesla hires deep learning expert Andrej Karpathy to lead Autopilot vision”

Fei-Fei Li of SVL and her work on ImageNet is featured in Quartz. “The data that transformed AI research—and possibly the world”

Join the Lab

Fei-Fei Li

Fei-Fei Li

(publishes under L.Fei-Fei)

Professor,

Director

Stanford AI Lab

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Juan Carlos Niebles

Juan Carlos Niebles

 

Senior Research Scientist,

Associate Director

Sail-Toyota Center for AI Research

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Silvio Savarese

Silvio Savarese

 

Associate Professor,

Director

Sail-Toyota Center for AI Research

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