Temporal Context Aggregation Network for Temporal Action Proposal Refinement (2103.13141v1)

Abstract: Temporal action proposal generation aims to estimate temporal intervals of actions in untrimmed videos, which is a challenging yet important task in the video understanding field. The proposals generated by current methods still suffer from inaccurate temporal boundaries and inferior confidence used for retrieval owing to the lack of efficient temporal modeling and effective boundary context utilization. In this paper, we propose Temporal Context Aggregation Network (TCANet) to generate high-quality action proposals through "local and global" temporal context aggregation and complementary as well as progressive boundary refinement. Specifically, we first design a Local-Global Temporal Encoder (LGTE), which adopts the channel grouping strategy to efficiently encode both "local and global" temporal inter-dependencies. Furthermore, both the boundary and internal context of proposals are adopted for frame-level and segment-level boundary regressions, respectively. Temporal Boundary Regressor (TBR) is designed to combine these two regression granularities in an end-to-end fashion, which achieves the precise boundaries and reliable confidence of proposals through progressive refinement. Extensive experiments are conducted on three challenging datasets: HACS, ActivityNet-v1.3, and THUMOS-14, where TCANet can generate proposals with high precision and recall. By combining with the existing action classifier, TCANet can obtain remarkable temporal action detection performance compared with other methods. Not surprisingly, the proposed TCANet won the 1$^{st}$ place in the CVPR 2020 - HACS challenge leaderboard on temporal action localization task.

Temporal Context Aggregation Network for Temporal Action Proposal Refinement

Temporal action proposal generation in untrimmed videos remains a challenge in video understanding due to issues such as inaccurate temporal boundaries and subpar proposal confidence. The paper introduces the Temporal Context Aggregation Network (TCANet), a method designed to produce high-quality action proposals via local and global temporal context aggregation alongside complementary and progressive boundary refinement.

The core of TCANet comprises two primary components: the Local-Global Temporal Encoder (LGTE) and the Temporal Boundary Regressor (TBR). LGTE employs a channel grouping strategy to efficiently encode local and global temporal inter-dependencies, capturing long-term context while maintaining boundary sensitivity through dynamic local modeling. The paper meticulously designates the channel grouping technique and divides input features into respective groups for local and global processing, thereby enabling diverse temporal relationship modeling within TCANet. The incorporation of FFN layers following the encoding process further enhances feature interactions across channels, contributing to the proposal's robustness and precision.

The TBR performs granular boundary refinement through simultaneous frame-level and segment-level regressions. This bifurcation allows TBR to capitalize on boundary sensitivity via frame-level regressions, utilizing starting and ending contexts, while segment-level regressions leverage internal proposal context for overall boundary perception. Adopting candidate proposals from highly competitive methods like BMN as inputs, TCANet implements a training regime grounded on complementary fusion and progressive boundary refinements, optimizing precision in the proposals.

Significant experimental validation was conducted on prominent datasets such as HACS, ActivityNet-v1.3, and THUMOS-14, illustrating TCANet's superior proposal precision and recall. Notably, on these datasets, TCANet demonstrated substantial improvements in mean Average Precision (mAP) across several Intersection-over-Union (IoU) thresholds. These enhanced results affirm the capability of TCANet in providing not only effective proposal generation but also meaningful contributions to the temporal action detection task when coupled with existing classifiers. The methodological approach embodies practical advancements in real-world applications, such as video content analysis and recommendation systems.

Implications of this research are multifaceted, encompassing both theoretical advancements in temporal modeling within video content and practical enhancements in action detection technologies. The strategic fusion of local detail sensitivity and global dependency awareness animates potential developments in related AI applications. Furthermore, the modular nature of TCANet suggests adaptability in future frameworks aiming to address similar challenges in complex temporal action recognition tasks.

In summary, the proposed TCANet offers an innovative approach to overcoming prevalent challenges in temporal action proposal generation, underscoring its wide applicability and effectiveness as evidenced by competitive leaderboard positions and significant improvements in established metrics. Future developments could expand TCANet's adaptability and efficiency amidst evolving video understanding paradigms.