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We use multilayer Long Short Term Memory (LSTM) networks to learnrepresentations of video sequences. Our model uses an encoder LSTM to map aninput sequence into a fixed length representation. This representation isdecoded using single or multiple decoder LSTMs to perform different tasks, suchas reconstructing the input sequence, or predicting the future sequence. Weexperiment with two kinds of input sequences - patches of image pixels andhigh-level representations ("percepts") of video frames extracted using apretrained convolutional net. We explore different design choices such aswhether the decoder LSTMs should condition on the generated output. We analyzethe outputs of the model qualitatively to see how well the model canextrapolate the learned video representation into the future and into the past.We try to visualize and interpret the learned features. We stress test themodel by running it on longer time scales and on out-of-domain data. We furtherevaluate the representations by finetuning them for a supervised learningproblem - human action recognition on the UCF-101 and HMDB-51 datasets. We showthat the representations help improve classification accuracy, especially whenthere are only a few training examples. Even models pretrained on unrelateddatasets (300 hours of YouTube videos) can help action recognition performance.

Unsupervised Learning of Video Representations using LSTMs