Temporal Attentive Alignment for Video Domain Adaptation (1905.10861v5)

Abstract: 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.

• The paper presents TA3N, a novel framework that integrates temporal attention with adversarial training to effectively address video domain shift challenges.
• The paper introduces the UCF-HMDB_full dataset, a large-scale benchmark offering higher domain discrepancies than previous datasets for rigorous evaluation.
• TA3N achieves impressive accuracy gains of 6.66% and 7.88% on cross-dataset tasks, demonstrating its effectiveness in aligning multi-scale temporal features.

Temporal Attentive Alignment for Video Domain Adaptation

In the domain of video-based unsupervised domain adaptation (DA), Min-Hung Chen et al. present a compelling approach called the Temporal Attentive Adversarial Adaptation Network (TA$^3$N). The paper addresses the challenges of domain shift in videos—a problem less explored than its image-based counterpart. Offering both a novel methodology and a new dataset, the paper establishes a significant step forward in video DA.

Key Contributions

1. UCF-HMDB$_\text{full}$ Dataset: The authors introduce UCF-HMDB$_\text{full}$, a larger-scale dataset with increased domain discrepancy compared to existing small-scale datasets like UCF-Olympic and UCF-HMDB$_\text{small}$. This dataset facilitates rigorous testing of DA algorithms, addressing the saturation problem observed with smaller datasets.
2. Temporal Attentive Adversarial Adaptation Network (TA$^3$N): Central to the paper is the TA$^3$N method, which integrates the alignment of temporal dynamics using an attention mechanism. This attention focuses on those temporal dynamics contributing most significantly to domain shift, thereby enhancing alignment efficiency and effectiveness.
3. Enhanced Evaluation on Video DA Datasets: The proposed TA$^3$N achieves state-of-the-art performance on multiple video DA datasets, demonstrating efficacy over both baseline and sophisticated existing methods. Notably, it excels in aligning temporal features with larger domain discrepancies, as evidenced by substantial accuracy gains on UCF-HMDB$_\text{full}$.

Methodological Insights

TA$^3$N’s architecture is underpinned by several innovations in integrating temporal dynamics with DA techniques. Unlike traditional approaches that focus solely on spatial features, TA$^3$N uses a temporal relation module that captures multi-scale temporal relations, replacing simple temporal pooling. Additionally, by employing adversarial training with attention mechanisms, TA$^3$N effectively optimizes attention weights towards components with lower domain entropy, indicative of significant domain discrepancies.

Numerical Results

On the UCF-HMDB$_\text{full}$ dataset, TA$^3$N reaches an impressive accuracy of 78.33% on the "UCF to HMDB" task and 81.79% on the reverse task, with absolute gains of 6.66% and 7.88% respectively. These results underline the method’s capability to handle large-scale discrepancies efficiently.

Implications and Future Directions

The introduction of a larger dataset paired with a robust method like TA$^3$N provides a fresh paradigm for tackling video DA. The attention mechanism, driven by domain discrepancies, suggests a promising direction for future research — particularly in fine-tuning the balance between spatial and temporal feature alignment. Further exploration could investigate the application of TA$^3$N to other domains, such as real-time video analysis and surveillance, where the dynamics of domain shift present unique challenges.

Conclusion

This paper sets a vital benchmark in video domain adaptation through innovative architectural contributions and a significantly robust dataset. It paves the way for future explorations into comprehensive models that effectively leverage temporal dynamics for domain alignment, thereby broadening the applicability and accuracy of video-based AI systems.