PWS-DVC: Enhancing Weakly Supervised Dense Video Captioning with Pretraining Approach

In recent times, there has been a notable increase in efforts to simultaneously comprehend vision and language, driven by the availability of video-related datasets and advancements in language models within the domain of natural language processing. Dense video captioning poses a significant challenge in understanding untrimmed video and generating several event-based sentences to describe the video. Numerous endeavors have been undertaken to enhance the efficacy of the dense video captioning task by the utilization of various approaches, such as bottom-up, top-down, parallel pipeline, pretraining, etc. In contrast, the weakly supervised dense video captioning method presents a highly promising strategy for generating dense video captions solely based on captions, without relying on any knowledge of ground-truth events, which distinguishes it from widely employed approaches. Nevertheless, this approach has a drawback that inadequate captions might hurt both event localization and captioning. This paper introduces PWS-DVC, a novel approach aimed at enhancing the performance of weakly supervised dense video captioning. PWS-DVC's event captioning module is initially trained on video-clip datasets, which are extensively accessible video datasets by leveraging the absence of ground-truth data during training. Subsequently, it undergoes fine-tuning specifically for dense video captioning. In order to demonstrate the efficacy of PWS-DVC, we conduct comparative experiments with state-of-the-art methods using the ActivityNet Captions dataset. The findings indicate that PWS-DVC exhibits improved performance in comparison to current approaches in weakly supervised dense video captioning.