Background-Click Supervision for Temporal Action Localization (2111.12449v1)

Abstract: Weakly supervised temporal action localization aims at learning the instance-level action pattern from the video-level labels, where a significant challenge is action-context confusion. To overcome this challenge, one recent work builds an action-click supervision framework. It requires similar annotation costs but can steadily improve the localization performance when compared to the conventional weakly supervised methods. In this paper, by revealing that the performance bottleneck of the existing approaches mainly comes from the background errors, we find that a stronger action localizer can be trained with labels on the background video frames rather than those on the action frames. To this end, we convert the action-click supervision to the background-click supervision and develop a novel method, called BackTAL. Specifically, BackTAL implements two-fold modeling on the background video frames, i.e. the position modeling and the feature modeling. In position modeling, we not only conduct supervised learning on the annotated video frames but also design a score separation module to enlarge the score differences between the potential action frames and backgrounds. In feature modeling, we propose an affinity module to measure frame-specific similarities among neighboring frames and dynamically attend to informative neighbors when calculating temporal convolution. Extensive experiments on three benchmarks are conducted, which demonstrate the high performance of the established BackTAL and the rationality of the proposed background-click supervision. Code is available at https://github.com/VividLe/BackTAL.

• The paper introduces background-click supervision to mitigate action-context confusion by focusing on non-action frames for clearer temporal localization.
• It employs a Score Separation Module and an Affinity Module to distinctly differentiate between action and background frames through improved score disparity and dynamic temporal adjustments.
• Empirical results on THUMOS14, ActivityNet, and HACS show that BackTAL outperforms existing weakly supervised methods, offering a favorable balance between annotation cost and accuracy.

Background-Click Supervision for Temporal Action Localization: A Summary

Temporal action localization in video data aims to accurately identify action instances by predicting start times, end times, and action category labels. Conventional methods in this domain have traditionally relied on either fully supervised datasets, which require extensive instance-level annotations, or weakly supervised approaches that utilize video-level labels. However, the latter faces significant challenges, primarily due to the action-context confusion problem, where non-action frames that share context with actions are difficult to distinguish.

In response to this challenge, the paper under review—entitled "Background-Click Supervision for Temporal Action Localization"—proposes a novel approach named BackTAL. This method introduces a new concept called "background-click supervision," which consists of annotating background frames instead of action frames as done in previous frameworks like SF-Net that utilize action-click supervision. The authors argue that significant performance bottlenecks in current models stem from misclassifying background frames as actions. Therefore, focusing on background frames can provide stronger localization by training the model more effectively than existing methods that primarily emphasize action frames.

Key Contributions

1. Background-Click Supervision: The paper asserts that annotating background frames offers a more efficient pathway to address the action-context confusion problem. This approach allows the model to better separate actions from non-actions without significantly increasing annotation costs.
2. Score Separation Module: This module is introduced to utilize the background-click annotations by separating the score responses between action frames and background frames effectively. By increasing the disparity in classification scores between action and background frames, this module ensures improved localization accuracy.
3. Affinity Module: Leveraging frame-specific similarities, this module dynamically adjusts the temporal convolution operations. By learning embeddings that separate action features from background features, the model can attend more precisely to relevant frames, further aiding in distinguishing between action and background segments.

Experimental Validation

The proposed BackTAL method is evaluated across multiple benchmark datasets, including THUMOS14, ActivityNet v1.2, and HACS. The results are compelling, demonstrating substantial improvement over existing weakly supervised methods, including those employing other forms of additional weak supervision, such as SF-Net. On THUMOS14, for example, BackTAL achieves an mAP of 36.3% at tIoU threshold 0.5, outperforming SF-Net significantly.

Beyond performance metrics, the paper discusses the trade-off between annotation cost and localization performance gain. It finds that background-click supervision offers a more favorable balance, providing significant improvement without increasing the annotation burden compared to weakly supervised action-click methods.

Implications and Future Directions

The implications of this research extend into more efficient and accurate models for temporal action localization under weak supervision. By effectively handling contextual confusions in video data, this methodology could be transformative for applications in smart surveillance, video summarization, and beyond. Furthermore, this approach could lead to innovations in other weakly supervised domains such as object detection and semantic segmentation, where contextual confusion similarly hampers performance.

Future work may explore the integration of this background-click approach with more complex architectures, investigate alternative forms of click-based supervision, or extend to multi-modal data inputs. Moreover, exploring ways to automatically generate effective proxies for background-clicks could reduce the need for human annotators, further reducing costs while maintaining high performance.

Overall, the introduction of background-click supervision signifies a promising advance in weakly supervised temporal action localization, offering both theoretical insights and practical strategies for overcoming longstanding challenges in the field.