RESEARCH

COMPUTER VISION

Long-Term Feature Banks for Detailed Video Understanding

June 18, 2019

Abstract

To understand the world, we humans constantly need to relate the present to the past, and put events in context. In this paper, we enable existing video models to do the same. We propose a long-term feature bank—supportive information extracted over the entire span of a video—to augment state-of-the-art video models that otherwise would only view short clips of 2-5 seconds. Our experiments demonstrate that augmenting 3D convolutional networks with a long-term feature bank yields state-of-the-art results on three challenging video datasets: AVA, EPIC-Kitchens, and Charades. Code is available online.

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AUTHORS

Written by

Ross Girshick

Chao-Yuan Wu

Christoph Feichtenhofer

Haoqi Fan

Kaiming He

Philipp Kräkenbühl

Publisher

CVPR

Research Topics

Computer Vision

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