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

arXiv: https://arxiv.org/abs/1904.08634 · HTML: https://mlsanity.com/paper/1904.08634/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2019.

## Built on by (most cited first, 3 of 3)

- [VideoXum: Cross-modal Visual and Textural Summarization of Videos](https://mlsanity.com/paper/2303.12060/) (2023)
- [System-status-aware Adaptive Network for Online Streaming Video Understanding](https://mlsanity.com/paper/2303.15742/) (2023)
- [Refining Action Boundaries for One-stage Detection](https://mlsanity.com/paper/2210.14284/) (2022)

## Abstract (arXiv)

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.