Actions Inspire Every Moment: Online Action-Augmented Dense Video Captioning
Keywords: Dense video captioning, Online dense video captioning
Abstract: Dense video captioning requires solving the challenging tasks of temporally localizing events and generating descriptive captions within long video sequences. Existing methods often struggle to capture the evolving context within video streams and to produce accurate temporal alignment. To address this, we propose an online retrieval-augmented approach that processes video segments incrementally while dynamically retrieving relevant action phrases from a pre-constructed action-text corpus. This enriches the contextual information for both the video representation and the subsequent text decoder, improving the caption generation. Additionally, we present image-based simulated video pretraining, which mitigates the reliance on extensive video datasets by using image-level text-paired data aligned with the online video captioning format. Our experiments on the ViTT, YouCook2, and ActivityNet benchmarks demonstrate that our model significantly outperforms both existing global and online methods, validating its effectiveness.
Supplementary Material: pdf
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 3294
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