# DDLSTM: Dual-Domain LSTM for Cross-Dataset Action Recognition

## TL;DR

This paper introduces DDLSTM, a novel recurrent network architecture that aligns temporal features across domains, improving cross-dataset action recognition accuracy by 3.5%.

## Contribution

It is the first to implement domain alignment in recurrent networks using cross-contaminated batch normalization for LSTMs.

## Key findings

- Outperforms standard LSTMs in cross-dataset action recognition
- Achieves an average 3.5% accuracy increase
- Effective in learning temporal dependencies across domains

## Abstract

Domain alignment in convolutional networks aims to learn the degree of layer-specific feature alignment beneficial to the joint learning of source and target datasets. While increasingly popular in convolutional networks, there have been no previous attempts to achieve domain alignment in recurrent networks. Similar to spatial features, both source and target domains are likely to exhibit temporal dependencies that can be jointly learnt and aligned. In this paper we introduce Dual-Domain LSTM (DDLSTM), an architecture that is able to learn temporal dependencies from two domains concurrently. It performs cross-contaminated batch normalisation on both input-to-hidden and hidden-to-hidden weights, and learns the parameters for cross-contamination, for both single-layer and multi-layer LSTM architectures. We evaluate DDLSTM on frame-level action recognition using three datasets, taking a pair at a time, and report an average increase in accuracy of 3.5%. The proposed DDLSTM architecture outperforms standard, fine-tuned, and batch-normalised LSTMs.