Leveraging auxiliary image descriptions for dense video captioning. Boran, E., Erdem, A., Ikizler-Cinbis, N., Erdem, E., Madhyastha, P., & Specia, L. Pattern Recognition Letters, 146:70-76, 2021.
Leveraging auxiliary image descriptions for dense video captioning [link]Paper  doi  abstract   bibtex   
Collecting textual descriptions is an especially costly task for dense video captioning, since each event in the video needs to be annotated separately and a long descriptive paragraph needs to be provided. In this paper, we investigate a way to mitigate this heavy burden and propose to leverage captions of visually similar images as auxiliary context. Our model successfully fetches visually relevant images and combines noun and verb phrases from their captions to generating coherent descriptions. To this end, we use a generator and discriminator design, together with an attention-based fusion technique, to incorporate image captions as context in the video caption generation process. The experiments on the challenging ActivityNet Captions dataset demonstrate that our proposed approach achieves more accurate and more diverse video descriptions compared to the strong baseline using METEOR, BLEU and CIDEr-D metrics and qualitative evaluations.
@article{BORAN202170,
    author = "Boran, Emre and Erdem, Aykut and Ikizler-Cinbis, Nazli and Erdem, Erkut and Madhyastha, Pranava and Specia, Lucia",
    title = "Leveraging auxiliary image descriptions for dense video captioning",
    journal = "Pattern Recognition Letters",
    volume = "146",
    pages = "70-76",
    year = "2021",
    issn = "0167-8655",
    doi = "https://doi.org/10.1016/j.patrec.2021.02.009",
    url = "https://www.sciencedirect.com/science/article/pii/S0167865521000647",
    keywords = "Video captioning,Adversarial training,Attention,CV",
    abstract = "Collecting textual descriptions is an especially costly task for dense video captioning, since each event in the video needs to be annotated separately and a long descriptive paragraph needs to be provided. In this paper, we investigate a way to mitigate this heavy burden and propose to leverage captions of visually similar images as auxiliary context. Our model successfully fetches visually relevant images and combines noun and verb phrases from their captions to generating coherent descriptions. To this end, we use a generator and discriminator design, together with an attention-based fusion technique, to incorporate image captions as context in the video caption generation process. The experiments on the challenging ActivityNet Captions dataset demonstrate that our proposed approach achieves more accurate and more diverse video descriptions compared to the strong baseline using METEOR, BLEU and CIDEr-D metrics and qualitative evaluations."
}

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