Computer Vision

Understanding the visual world around us

Computer Vision researchers at Facebook are inventing new ways for computers to gain a higher level of understanding cued from the visual world around us.

We are creating visual sensors derived from digital images and videos that extract information about our environment, to further enable Facebook services to automate tasks that people automatically do today visually. Our ultimate goal, to automatically, and intelligently enhance people’s experiences across Facebook products.

Latest Publications

All Publications

PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization

Shunsuke Saito, Tomas Simon, Jason Saragih, Hanbyul Joo

CVPR - June 16, 2020

Epipolar Transformers

Yihui He, Rui Yan, Katerina Fragkiadaki, Shoou-I Yu

CVPR - June 16, 2020

ARCH: Animatable Reconstruction of Clothed Humans

Zeng Huang, Yuanlu Xu, Christoph Lassner, Hao Li, Tony Tung

CVPR - June 15, 2020

ViBE: Dressing for Diverse Body Shapes

Wei-Lin Hsiao, Kristen Grauman

CVPR - June 14, 2020

Downloads & Projects

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This repository contains a Torch implementation for the ResNeXt algorithm for image classification. ResNeXt is a simple, highly modularized network architecture for image classification.

Existing methods for visual reasoning attempt to directly map inputs to outputs using black-box architectures without explicitly modeling the underlying reasoning processes. As a result, these black-box models often learn to exploit biases in the data rather than learning to perform visual reasoning.

Low-shot visual learning—the ability to recognize novel object categories from very few examples—is a hallmark of human visual intelligence.

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