VideoGrounding-DINO: Towards Open-Vocabulary Spatio- Temporal Video Grounding

Syed Talal Wasim, Muzammal Naseer, Salman H. Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan

Abstract: 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 pre-defined vocabularies, our model leverages pre-trained rep-resentations from foundational spatial grounding models. This empowers it to effectively bridge the semantic gap be-tween 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 demon-strating superior performance in open-vocabulary scenar-ios. Notably, the proposed model outperforms state-of-the-art methods in closed-set settings on VidSTG (Declarative and Interrogative) and HC-STVG (VI and V2) datasets. Furthermore, in open-vocabulary evaluations on HC-STVG VI and YouCook-Interactions, our model surpasses the re-cent best-performing models by 4.88 m.vloU and 1.83% ac-curacy, demonstrating its efficacy in handling diverse lin-guistic and visual concepts for improved video understanding. Our codes will be publicly released.