The content introduces a new method for dense video grounding, focusing on localizing multiple moments with a paragraph as input. The proposed PRVG framework simplifies the process by directly predicting temporal boundaries for each sentence query, leading to efficient and accurate results.
Existing methods often address video grounding indirectly, resulting in complicated label assignment and near-duplicate removal. The proposed PRVG eliminates these issues by regressing only one moment for each sentence query. This approach shows superiority over other methods in experiments on ActivityNet Captions and TACoS datasets.
The content discusses the importance of capturing semantic relevance among multiple sentences in a paragraph for accurate localization in dense video grounding. It also highlights the effectiveness of the proposal-level attention loss designed to guide model training for better performance.
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by Fengyuan Shi... at arxiv.org 02-29-2024
https://arxiv.org/pdf/2109.11265.pdfDeeper Inquiries