  Open-vocabulary Temporal Action Detection (Open-vocab TAD) is an advanced
video analysis approach that expands Closed-vocabulary Temporal Action
Detection (Closed-vocab TAD) capabilities. Closed-vocab TAD is typically
confined to localizing and classifying actions based on a predefined set of
categories. In contrast, Open-vocab TAD goes further and is not limited to
these predefined categories. This is particularly useful in real-world
scenarios where the variety of actions in videos can be vast and not always
predictable. The prevalent methods in Open-vocab TAD typically employ a 2-stage
approach, which involves generating action proposals and then identifying those
actions. However, errors made during the first stage can adversely affect the
subsequent action identification accuracy. Additionally, existing studies face
challenges in handling actions of different durations owing to the use of fixed
temporal processing methods. Therefore, we propose a 1-stage approach
consisting of two primary modules: Multi-scale Video Analysis (MVA) and
Video-Text Alignment (VTA). The MVA module captures actions at varying temporal
resolutions, overcoming the challenge of detecting actions with diverse
durations. The VTA module leverages the synergy between visual and textual
modalities to precisely align video segments with corresponding action labels,
a critical step for accurate action identification in Open-vocab scenarios.
Evaluations on widely recognized datasets THUMOS14 and ActivityNet-1.3, showed
that the proposed method achieved superior results compared to the other
methods in both Open-vocab and Closed-vocab settings. This serves as a strong
demonstration of the effectiveness of the proposed method in the TAD task.
