Bidirectional Temporal-Sensitive Adaptation for Generalized Zero-Shot Temporal Action Localization

Zero-shot temporal action localization (ZSTAL) aims to localize and recognize action categories unseen during training. However, it assumes that test videos contain only unseen classes, which is unrealistic in practice where seen and unseen actions naturally co-exist. To bridge this gap, we introduce generalized ZSTAL (GZS-TAL), where models trained only on seen classes must handle both seen and unseen ones during testing. This setting highlights a critical challenge: a static, frozen model cannot adapt to the mixed distributions encountered at test time. To address this issue, we propose a Temporal-Sensitive Adaptation (TSA) module that equips TAL models with the ability to update themselves during testing. The key intuition is to use temporal dependency prediction as a self-supervised signal: TSA introduces an online-updatable memory optimized to reconstruct features of preceding segments from the current one, thereby embedding temporal dependencies into parameters and reusing them for adaptation at test time. To further enhance temporal modeling, we extend TSA into a Bi-directional TSA (Bi-TSA) mechanism that performs prediction in both forward and backward directions. By simultaneously exploiting historical and future contexts, Bi-TSA improves long-range temporal representation and yields more accurate boundary localization. Extensive experiments on THUMOS14 and ActivityNet-1.3 demonstrate that our approach achieves significant improvements over state-of-the-art methods under the GZS-TAL setting, validating its effectiveness and generalization ability.