  Video grounding aims to localize a spatio-temporal section in a video
corresponding to an input text query. This paper addresses a critical
limitation in current video grounding methodologies by introducing an
Open-Vocabulary Spatio-Temporal Video Grounding task. Unlike prevalent
closed-set approaches that struggle with open-vocabulary scenarios due to
limited training data and predefined vocabularies, our model leverages
pre-trained representations from foundational spatial grounding models. This
empowers it to effectively bridge the semantic gap between natural language and
diverse visual content, achieving strong performance in closed-set and
open-vocabulary settings. Our contributions include a novel spatio-temporal
video grounding model, surpassing state-of-the-art results in closed-set
evaluations on multiple datasets and demonstrating superior performance in
open-vocabulary scenarios. Notably, the proposed model outperforms
state-of-the-art methods in closed-set settings on VidSTG (Declarative and
Interrogative) and HC-STVG (V1 and V2) datasets. Furthermore, in
open-vocabulary evaluations on HC-STVG V1 and YouCook-Interactions, our model
surpasses the recent best-performing models by $4.88$ m_vIoU and $1.83\%$
accuracy, demonstrating its efficacy in handling diverse linguistic and visual
concepts for improved video understanding. Our codes will be publicly released.
