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Title: Convolutional Tensor-Train LSTM for Long-Term Video Prediction

Paper content:
Long-term video prediction is highly challenging since it entails simultaneously capturing spatial and temporal information across a long range of image frames.Standard recurrent models are ineffective since they are prone to error propagation and cannot effectively capture higher-order correlations.
A potential solution is to extend to higher-order spatio-temporal recurrent models.
However, such a model requires  a  large number of parameters and operations, making it intractable  to learn in practice and is prone to overfitting.
In this work, we propose convolutional tensor-train LSTM (Conv-TT-LSTM), which  learns higher-orderConvolutional LSTM (ConvLSTM) efficiently using convolutional  tensor-train decomposition (CTTD).
Our proposed model naturally incorporates higher-order spatio-temporal information at a small cost of memory and computation by using efficient low-rank tensor representations.
We evaluate our model on Moving-MNIST and KTH datasets and show improvements over standard ConvLSTM and better/comparable results to other ConvLSTM-based approaches, but with much fewer parameters.
Understanding dynamics of videos and performing long-term predictions of the future is a highly challenging problem.
It entails learning complex representation of real-world environment without external supervision.
This arises in a wide range of applications, including autonomous driving, robot control , or other visual perception tasks like action recognition or object tracking (Alahi et al., 2016) .
However, long-term video prediction remains an open problem due to high complexity of the video contents.
Therefore, prior works mostly focus on next or first few frames prediction (Lotter et al., 2016; Finn et al., 2016; Byeon et al., 2018) .
Many recent video models use Convolutional LSTM (ConvLSTM) as a basic block (Xingjian et al., 2015) , where spatio-temporal information is encoded as a tensor explicitly in each cell.
In ConvL-STM networks, each cell is a first-order recurrent model, where the hidden state is updated based on its immediate previous step.
Therefore, they cannot easily capture higher-order temporal correlations needed for long-term prediction.
Moreover, they are highly prone to error propagation.
Various approaches have been proposed to augment ConvLSTM, either by modifying networks to explicitly modeling motion (Finn et al., 2016) , or by integrating spatio-temporal interaction in ConvLSTM cells (Wang et al., 2017; 2018a) .
These approaches are often incapable of capturing longterm dependencies and produce blurry prediction.
Another direction to augment ConvLSTM is to incorporate a higher-order RNNs (Soltani & Jiang, 2016) inside each LSTM cell, where its hidden state is updated using multiple past steps.
However, a higher-order model for high-dimensional data (e.g. video) requires a huge number of model parameters, and the computation grows exponentially with the order of the RNNs.
A principled approach to address the curse of dimensionality is tensor decomposition, where a higher-order tensor is compressed into smaller core tensors (Anandkumar et al., 2014) .
Tensor representations are powerful since they retain rich expressivity even with a small number of parameters.
In this work, we propose a novel convolutional tensor decomposition, which allows for compact higher-order ConvLSTM.
Contributions.
We propose Convolutional Tensor-Train LSTM (Conv-TT-LSTM), a modification of ConvLSTM, to build a higher-order spatio-temporal model.
(1) We introduce Convolutional Tensor-Train Decomposition (CTTD) that factorizes a large convolutional kernel into a chain of
Figure 1: Illustration of
(a) convolutional tensor-train (Eqs.
(5) and (6)) and the difference between convolutional tensor-train LSTM
(b) Fixed window version (Eqs.
(11a) and (10)) and
(c) Sliding window version (Eqs.
(11b) and (10) and 1c ), and we found that the SW version performs better than the FW one.
(4) We found that training higher-order tensor models is not straightforward due to gradient instability.
We present several approaches to overcome this such as good learning schedules and gradient clipping.
(5) In the experiments, we show our proposed Conv-TT-LSTM consistently produces sharp prediction over a long period of time for both Moving-MNIST-2 and KTH action datasets.
Conv-TT-LSTM outperforms the state-of-the-art PredRNN++ (Wang et al., 2018a) in LPIPS (Zhang et al., 2018) by 0.050 on the Moving-MNIST-2 and 0.071 on the KTH action dataset, with 5.6 times fewer parameters.
Thus, we obtain best of both worlds: better long-term prediction and model compression.
In this paper, we proposed convolutional tensor-train decomposition to factorize a large convolutional kernel into a set of smaller core tensors.
We applied this technique to efficiently construct convolutional tensor-train LSTM (Conv-TT-LSTM), a high-order spatio-temporal recurrent model whose parameters are represented in tensor-train format.
We empirically demonstrated that our proposed Conv-TT-LSTM outperforms standard ConvLSTM and produce better/comparable results compared to other state-of-the-art models with fewer parameters.
Utilizing the proposed model for high-resolution videos is still challenging due to gradient vanishing or explosion.
Future direction will include investigating other training strategies or a model design to ease the training process.
In this section, we prove the sequential algorithms in Eq. (3) for tensor-train decomposition (1) and Eq. (6) for convolutional tensor-train decomposition (4) both by induction.
Proof of Eq. (3) For simplicity, we denote the standard tensor-train decomposition in Eq. (1) as
), then Eq. (2) can be rewritten as Eq. (12) since R 0 = 1 and v
i1,r0,r1 · · · T
where R 0 = 1, v

assistant: text: we propose convolutional tensor-train LSTM,  which learns higher-order Convolutional LSTM efficiently using convolutional tensor-train decomposition.