  Temporal Action Localization (TAL) is a critical task in video analysis,
identifying precise start and end times of actions. Existing methods like CNNs,
RNNs, GCNs, and Transformers have limitations in capturing long-range
dependencies and temporal causality. To address these challenges, we propose a
novel TAL architecture leveraging the Selective State Space Model (S6). Our
approach integrates the Feature Aggregated Bi-S6 block, Dual Bi-S6 structure,
and a recurrent mechanism to enhance temporal and channel-wise dependency
modeling without increasing parameter complexity. Extensive experiments on
benchmark datasets demonstrate state-of-the-art results with mAP scores of
74.2% on THUMOS-14, 42.9% on ActivityNet, 29.6% on FineAction, and 45.8% on
HACS. Ablation studies validate our method's effectiveness, showing that the
Dual structure in the Stem module and the recurrent mechanism outperform
traditional approaches. Our findings demonstrate the potential of S6-based
models in TAL tasks, paving the way for future research.
