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This paper centers on pushing the boundaries of AI and computer vision through an inventive approach to video captioning, leveraging the powerful Contrastive Language Image Pretraining (CLIP) model. In stark contrast to conventional methods, our proposed system ingeniously merges CLIP's unique ability to comprehend both textual and visual components, establishing a unified embedding space. This integration results in heightened context awareness and semantic depth, revolutionizing the comprehension of video content and outperforming traditional approaches. The AI-enhanced video captioning system represents a significant leap forward in the realm of generating precise and meaningful captions for a wide array of videos. The distinctive feature of our system lies in its preprocessing modules, adept at extracting intricate visual features and encoding nuanced textual descriptions. Additionally, we fine-tune the CLIP model on an extensive dataset of video-caption pairs, allowing it to capture complex relationships between visual and textual elements. This approach ensures that the model not only excels in providing accurate captions for diverse video content but also exhibits a remarkable ability to generalize well to previously unseen data. The result is a robust and contextually relevant captioning system that contributes to the evolution of AI applications in video understanding.
Aravind et al. (Thu,) studied this question.