Learning Disentangled Representations of Video with Missing Data

• URL: http://arxiv.org/abs/2006.13391v2
• Date: Tue, 3 Nov 2020 20:56:04 GMT
• Title: Learning Disentangled Representations of Video with Missing Data
• Authors: Armand Comas-Massagu\'e, Chi Zhang, Zlatan Feric, Octavia Camps, Rose Yu
• Abstract summary: We present Disentangled Imputed Video autoEncoder (DIVE), a deep generative model that imputes and predicts future video frames in the presence of missing data. Specifically, DIVE introduces a missingness latent variable, disentangles the hidden video representations into static and dynamic appearance, pose, and missingness factors for each object. On a moving MNIST dataset with various missing scenarios, DIVE outperforms the state of the art baselines by a substantial margin.
• Score: 17.34839550557689
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Missing data poses significant challenges while learning representations of video sequences. We present Disentangled Imputed Video autoEncoder (DIVE), a deep generative model that imputes and predicts future video frames in the presence of missing data. Specifically, DIVE introduces a missingness latent variable, disentangles the hidden video representations into static and dynamic appearance, pose, and missingness factors for each object. DIVE imputes each object's trajectory where data is missing. On a moving MNIST dataset with various missing scenarios, DIVE outperforms the state of the art baselines by a substantial margin. We also present comparisons for real-world MOTSChallenge pedestrian dataset, which demonstrates the practical value of our method in a more realistic setting. Our code and data can be found at https://github.com/Rose-STL-Lab/DIVE.

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