# Temporal Attentive Alignment for Video Domain Adaptation

arXiv: https://arxiv.org/abs/1905.10861 · HTML: https://mlsanity.com/paper/1905.10861/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2019.

## Built on by (most cited first, 1 of 1)

- [What can a cook in Italy teach a mechanic in India? Action Recognition Generalisation Over Scenarios and Locations](https://mlsanity.com/paper/2306.08713/) (2023)

## Abstract (arXiv)

Although various image-based domain adaptation (DA) techniques have been proposed in recent years, domain shift in videos is still not well-explored. Most previous works only evaluate performance on small-scale datasets which are saturated. Therefore, we first propose a larger-scale dataset with larger domain discrepancy: UCF-HMDB_full. Second, we investigate different DA integration methods for videos, and show that simultaneously aligning and learning temporal dynamics achieves effective alignment even without sophisticated DA methods. Finally, we propose Temporal Attentive Adversarial Adaptation Network (TA3N), which explicitly attends to the temporal dynamics using domain discrepancy for more effective domain alignment, achieving state-of-the-art performance on three video DA datasets. The code and data are released at http://github.com/cmhungsteve/TA3N.