Temporal Attentive Alignment for Large-Scale Video Domain Adaptation (1907.12743v6)

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 two large-scale video DA datasets with much larger domain discrepancy: UCF-HMDB_full and Kinetics-Gameplay. 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 four video DA datasets (e.g. 7.9% accuracy gain over "Source only" from 73.9% to 81.8% on "HMDB --> UCF", and 10.3% gain on "Kinetics --> Gameplay"). The code and data are released at http://github.com/cmhungsteve/TA3N.

Temporal Attentive Alignment for Large-Scale Video Domain Adaptation

The paper "Temporal Attentive Alignment for Large-Scale Video Domain Adaptation" delivers a comprehensive exploration into video domain adaptation (DA), a less explored facet of domain adaptation, which traditionally focuses on image data. The authors introduce innovative datasets and methodologies to address challenges associated with domain shift in video applications.

Core Contributions

The authors make three significant contributions:

1. Dataset Development: The paper introduces two substantial video DA datasets, UCF-HMDB$_{full}$ and Kinetics-Gameplay. These datasets are designed to provide a broader domain discrepancy than previous datasets, facilitating more accurate assessments of DA techniques. The UCF-HMDB$_{full}$ dataset extends the previously limited UCF-HMDB$_{small}$ dataset, offering twelve overlapping categories between UCF101 and HMDB51. In contrast, Kinetics-Gameplay involves virtual versus real-world domains, incorporating gameplay data and overlapping categories with Kinetics-600.
2. Temporal Feature Alignment: The investigation into temporal dynamics reveals that aligning temporal features is more critical than merely selecting sophisticated DA methods. By encoding temporal dynamics into video features, their adapted methodology outperforms conventional spatial feature alignment approaches. This concept is crystallized in the Temporal Adversarial Adaptation Network (TA$^2$N), which aligns both spatial and temporal features concurrently.
3. Temporal Attentive Adversarial Adaptation Network (TA$^3$N): This proposed method advances by attending to temporal discrepancy. Utilizing a domain attention mechanism that focuses on temporal dynamics exhibiting significant domain distribution discrepancy, TA$^3$N achieves state-of-the-art results on all evaluated datasets. Notably, TA$^3$N enhances accuracy on datasets such as HMDB $\rightarrow$ UCF with gains of up to 7.88%, and Kinetics $\rightarrow$ Gameplay with a 10.28% increase.

Methodological Insights

The methodology hinges on two main considerations: effectively integrating temporal dynamics and utilizing adversarial mechanisms for alignment. The Temporal Relation module encodes multiscale temporal relations, outperforming simpler pooling mechanisms, which fall short in capturing intricate temporal dependencies. The adversarial strategy employed breaks traditional DA integration, aligning domain features end-to-end and enhancing overall video representation robustness against domain shifts.

Implications and Future Work

This work opens avenues for leveraging large-scale video datasets to enrich DA research. By focusing on temporal alignment, it sets a precedent for addressing domain shift beyond static images, pushing the envelope in fields directly benefiting from video data, such as autonomous navigation, surveillance, and virtual training environments.

Future research should explore open-set DA settings, where source and target domain categories differ, reflecting real-world scenarios more accurately. Extending TA$^3$N to varied video tasks such as segmentation or captioning could reveal broader applications, integrating diverse domain adaptation techniques to broaden its utility.

The implications of this research extend into enhancing robust AI systems capable of learning from diverse, constantly evolving video domains, illustrating meaningful strides toward more generalized video processing methodologies.