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Extending Long Short-Term Memory for Multi-View Structured Learning

Extending Long Short-Term Memory for Multi-View Structured Learning

S. Rajagopalan, L.-P. Morency, T. Baltrus̆aitis and R. Goecke, Extending Long Short-Term Memory for Multi-View Structured Learning, In Proceedings of the European Conference on Computer Vision (ECCV), 2016

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Typical techniques for sequence modeling rely upon well-segmented sequences which have been edited to remove noisy or irrelevant parts. Therefore, we cannot easily apply such methods to noisy sequences expected in real-world applications.

We study sequence modeling through the combination of RNNs that captures the temporal dependencies and the attention mechanism that localizes the salient observations which are relevant to the final decision and ignore the irrelevant (noisy) parts of the input sequence.