Zero-Shot Temporal Action Localization (ZS-TAL) seeks to identify and locate
actions in untrimmed videos unseen during training. Existing ZS-TAL methods
involve fine-tuning a model on a large amount of annotated training data. While
effective, training-based ZS-TAL approaches assume the availability of labeled
data for supervised learning, which can be impractical in some applications.
Furthermore, the training process naturally induces a domain bias into the
learned model, which may adversely affect the model's generalization ability to
arbitrary videos. These considerations prompt us to approach the ZS-TAL problem
from a radically novel perspective, relaxing the requirement for training data.
To this aim, we introduce a novel method that performs Test-Time adaptation for
Temporal Action Localization (T3AL). In a nutshell, T3AL adapts a pre-trained
Vision and Language Model (VLM). T3AL operates in three steps. First, a
video-level pseudo-label of the action category is computed by aggregating
information from the entire video. Then, action localization is performed
adopting a novel procedure inspired by self-supervised learning. Finally,
frame-level textual descriptions extracted with a state-of-the-art captioning
model are employed for refining the action region proposals. We validate the
effectiveness of T3AL by conducting experiments on the THUMOS14 and the
ActivityNet-v1.3 datasets. Our results demonstrate that T3AL significantly
outperforms zero-shot baselines based on state-of-the-art VLMs, confirming the
benefit of a test-time adaptation approach.

通过引入一种新的方法 (T3AL)，该方法对 Temporal Action Localization (TAL) 进行 Test-Time adaptation，并采用自我监督学习的启发式程序进行动作区域定位，使用最先进的字幕模型提取的帧级文本描述进一步完善动作区域提案，实验证明 T3AL 在 THUMOS14 和 ActivityNet-v1.3 数据集上明显优于基于最先进视觉语言模型的零样本方法，证实了测试时间自适应方法的益处。