Revisiting Foreground and Background Separation in Weakly-supervised Temporal Action Localization: A Clustering-based Approach (2312.14138v1)

Abstract: Weakly-supervised temporal action localization aims to localize action instances in videos with only video-level action labels. Existing methods mainly embrace a localization-by-classification pipeline that optimizes the snippet-level prediction with a video classification loss. However, this formulation suffers from the discrepancy between classification and detection, resulting in inaccurate separation of foreground and background (F&B) snippets. To alleviate this problem, we propose to explore the underlying structure among the snippets by resorting to unsupervised snippet clustering, rather than heavily relying on the video classification loss. Specifically, we propose a novel clustering-based F&B separation algorithm. It comprises two core components: a snippet clustering component that groups the snippets into multiple latent clusters and a cluster classification component that further classifies the cluster as foreground or background. As there are no ground-truth labels to train these two components, we introduce a unified self-labeling mechanism based on optimal transport to produce high-quality pseudo-labels that match several plausible prior distributions. This ensures that the cluster assignments of the snippets can be accurately associated with their F&B labels, thereby boosting the F&B separation. We evaluate our method on three benchmarks: THUMOS14, ActivityNet v1.2 and v1.3. Our method achieves promising performance on all three benchmarks while being significantly more lightweight than previous methods. Code is available at https://github.com/Qinying-Liu/CASE

• The paper introduces a clustering-centric strategy that leverages unsupervised snippet clustering to improve foreground and background separation beyond traditional classification losses.
• It employs an optimal transport-based self-labeling mechanism to generate high-quality pseudo-labels, ensuring accurate alignment between snippet clusters and action regions.
• Evaluations on THUMOS14 and ActivityNet demonstrate competitive accuracy and enhanced computational efficiency compared to existing weakly-supervised approaches.

Overview of "Revisiting Foreground and Background Separation in Weakly-supervised Temporal Action Localization: A Clustering-based Approach"

The paper introduces a novel clustering-based approach for addressing the task of weakly-supervised temporal action localization (WTAL). WTAL involves identifying and localizing action instances in videos using only video-level action labels, without detailed frame annotations. A significant challenge in WTAL is accurately distinguishing between foreground (action) and background snippets solely based on video-level labels. Traditional methods often rely heavily on classification pipelines optimized via video classification loss, which are likely ineffective in action localization due to the inherent disparity between classification and detection tasks. The paper proposes a method that emphasizes unsupervised snippet clustering for enhanced foreground-background separation, aiming to discover the intrinsic structure among video snippets beyond the reliance on a video classification loss.

Key Contributions

1. Clustering-based F{Content}B Separation: The proposed method innovates on snippet clustering as the fundamental backbone for foreground and background (F{content}B) snippet separation. It incorporates a clustering mechanism followed by a cluster classification component, which categorizes clusters into foreground or background.
2. Self-labeling Mechanism via Optimal Transport: To facilitate clustering without ground-truth annotations, the authors developed a self-labeling strategy. This approach utilizes optimal transport theory to generate high-quality pseudo-labels which adhere to plausible prior distributions, thereby enabling a more accurate association between snippet cluster assignments and F{content}B labels.
3. Efficiency and Performance: Implementation of this method on datasets such as THUMOS14 and ActivityNet v1.2/v1.3 shows compelling results, achieving high accuracy while maintaining computational efficiency. The clustering-based approach is presented as significantly more lightweight compared to existing methods while yielding promising performance improvements.

Theoretical and Practical Implications

• By viewing F{content}B separation as a clustering problem, the approach broadens the horizon for tasks dependent on limited supervisory signals, showcasing that unsupervised clustering holds potential in reducing reliance on traditional classification losses.
• The proposal of optimal transport for pseudo-label generation offers a robust foundation for tasks beyond WTAL, emphasizing a transferable methodology across domains where similar separations are required without extensive labeled data.
• Practically, the reduced requirement for manually annotated training data streamlines the deployment of action localization frameworks, potentially enabling scalability in commercial and real-world video processing applications.

Future Developments

The incorporation of self-supervised learning techniques like contrastive clustering or advanced self-supervised loss functions might further enhance the clustering accuracy, especially as the need for high-resolution temporal localization in varied video types grows. Also, extending this framework to multi-modal datasets could leverage rich cross-modal information inherent in complex environments, promising further robustness in temporal action localization tasks. Although the current method primarily addresses two-class separation (foreground and background), exploring its extension towards multi-class segmentation would represent a logical progression, expanding its applicability in comprehensive video understanding pipelines.