Connecting Words to Motion: Unleashing Video Highlights and Moments Through Cross Attention Temporal Grounding

Recognizing significant moment and highlight detection, a crucial task in video analysis and understanding, aims to bridge the semantic gap between textual queries and video content, enabling efficient and effective searching within large-scale video collections. In recent times, notable progress has been achieved in recognizing significant moment, propelled by the swift expansion of online video platforms and the rising need for sophisticated video search systems. This paper introduces the inaugural comprehensive framework, denominated video-language temporal grounding (VTG), proficient in effecting joint optimization for such tasks while also easily adaptable for addressing individual problems with multi-modal transformers. To begin with, we examine various video-language temporal grounding tasks and labels and establish a cohesive formulation. Building upon this foundation, we design data annotation schemes to establish scalable pseudo-supervision. Secondly, we construct a versatile grounding model that proves effective in handling each task and fully leveraging every label. Finally, leveraging the universal framework, which unlock temporal grounding pretraining using a versatile set of large-scale labels, thereby enhancing grounding abilities, such as zero-shot grounding. We have achieved a remarkable improvement of overall 3–5% in mAP metrics. Our repository of codes is available at this link: https://github.com/hkhitesh7/VTG-using-CAT-Transformer.