Geometry Guided Convolutional Neural Networks for Self-Supervised Video Representation Learning

CVPR 2018 Chuang GanBoqing GongKun LiuHao SuLeonidas J. Guibas

It is often laborious and costly to manually annotate videos for training high-quality video recognition models, so there has been some work and interest in exploring alternative, cheap, and yet often noisy and indirect, training signals for learning the video representations. However, these signals are still coarse, supplying supervision at the whole video frame level, and subtle, sometimes enforcing the learning agent to solve problems that are even hard for humans... (read more)

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