# DDLSTM: Dual-Domain LSTM for Cross-Dataset Action Recognition Source: [DDLSTM: Dual-Domain LSTM for Cross-Dataset Action Recognition]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:31:37.819Z Authors: Toby Perrett, Dima Damen Published: 2019-04-18T08:17:35.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Online action recognition on 50Salads (test)](): Online action recognition; 50Salads (test); Accuracy: 41.1. ## Original sources and implementation [Original paper]() [PDF]()