Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation (2003.02824v3)

Abstract: Despite the recent progress of fully-supervised action segmentation techniques, the performance is still not fully satisfactory. One main challenge is the problem of spatiotemporal variations (e.g. different people may perform the same activity in various ways). Therefore, we exploit unlabeled videos to address this problem by reformulating the action segmentation task as a cross-domain problem with domain discrepancy caused by spatio-temporal variations. To reduce the discrepancy, we propose Self-Supervised Temporal Domain Adaptation (SSTDA), which contains two self-supervised auxiliary tasks (binary and sequential domain prediction) to jointly align cross-domain feature spaces embedded with local and global temporal dynamics, achieving better performance than other Domain Adaptation (DA) approaches. On three challenging benchmark datasets (GTEA, 50Salads, and Breakfast), SSTDA outperforms the current state-of-the-art method by large margins (e.g. for the F1@25 score, from 59.6% to 69.1% on Breakfast, from 73.4% to 81.5% on 50Salads, and from 83.6% to 89.1% on GTEA), and requires only 65% of the labeled training data for comparable performance, demonstrating the usefulness of adapting to unlabeled target videos across variations. The source code is available at https://github.com/cmhungsteve/SSTDA.

• The paper presents the SSTDA method, a self-supervised approach that realigns spatio-temporal features to enhance cross-domain action segmentation.
• It integrates binary and sequential domain prediction tasks to effectively harmonize local and global temporal dynamics, outperforming previous approaches.
• Experimental results on GTEA, 50Salads, and Breakfast datasets show significant performance boosts while reducing labeled data requirements to 65%.

Overview of "Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation"

The paper, "Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation," presents a novel methodology to address the outstanding challenges in the domain of action segmentation, particularly the spatio-temporal variations that obstruct the transferability across different video domains. By leveraging unlabeled videos, the authors reformulate the action segmentation task as a cross-domain adaptation problem, driven by domain discrepancies arising from these spatio-temporal inconsistencies.

The central contribution is the proposed Self-Supervised Temporal Domain Adaptation (SSTDA) method, which integrates self-supervised auxiliary tasks. These tasks, encompassing binary and sequential domain prediction, facilitate the cross-domain alignment of feature spaces laden with local and global temporal dynamics. This approach achieves notable performance improvements over existing Domain Adaptation (DA) strategies.

Experiments conducted on benchmark datasets—GTEA, 50Salads, and Breakfast—demonstrate substantial advancements in performance metrics, such as the F1@25 score, achieving increases from 59.6% to 69.1% on the Breakfast dataset, 73.4% to 81.5% on 50Salads, and 83.6% to 89.1% on GTEA, when compared to the current state-of-the-art techniques. Notably, SSTDA accomplishes these improvements while requiring only 65% of the labeled training data to maintain competitive results, underscoring its efficiency in leveraging unlabeled target videos to reduce domain variations.

Technical Contributions

1. Self-Supervised Sequential Domain Prediction: This task predicts the domain sequence for video segments in a long, untrimmed video, facilitating the adaptation of video domain representations. It introduces a novel self-supervised method tailored for cross-domain action segmentation.
2. Self-Supervised Temporal Domain Adaptation: The integration of binary and sequential domain prediction tasks within SSTDA helps in effectively harmonizing local and global embedded feature spaces between domains, surpassing other DA methodologies in performance.
3. Empirical Validation on Action Segmentation: By incorporating SSTDA, the method exhibits superior performance to current state-of-the-art techniques by significant margins across all tested metrics and datasets, whilst reducing the need for excessive labeled data.

Implications and Future Directions

The SSTDA framework illuminates a promising avenue for addressing domain shifts in video-based tasks, offering a methodological advancement that efficiently exploits unlabeled data. Its application extends beyond action segmentation, potentially transformable to other video-centric domains such as spatio-temporal action localization, thus broadening the scope for future research.

Theoretically, SSTDA proposes a compelling framework to reduce the reliance on manually annotated datasets, enhancing the practicality and scalability of action recognition systems. The methodology's self-supervised nature not only curtails labor-intensive labeling efforts but also introduces adaptability, addressing diverse operational scenarios.

Going forward, further exploration into the adaptability of this method in even more complex, real-world dynamic settings could bolster its robustness and applicability. Additionally, integrating SSTDA into multi-modal understanding systems could pave the way for developing advanced AI systems capable of nuanced understanding and interaction within varied environments.

In conclusion, this paper contributes a substantial enhancement to the action segmentation field, providing a comprehensive strategy to overcome the conventional limitations posed by cross-domain variances through self-supervised learning paradigms. As such, it holds significant promise for advancing video-based AI technologies.