Klein, Franz ; Mahajan, Shweta ; Roth, Stefan (2021)
Diverse Image Captioning with Grounded Style.
43rd German Conference on Pattern Recognition (GCPR 2021). (28.09.2021-01.10.2021)
doi: 10.1007/978-3-030-92659-5_27
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Stylized image captioning as presented in prior work aims to generate captions that reflect characteristics beyond a factual description of the scene composition, such as sentiments. Such prior work relies on given sentiment identifiers, which are used to express a certain global style in the caption, e.g. positive or negative, however without taking into account the stylistic content of the visual scene. To address this shortcoming, we first analyze the limitations of current stylized captioning datasets and propose COCO attribute-based augmentations to obtain varied stylized captions from COCO annotations. Furthermore, we encode the stylized information in the latent space of a Variational Autoencoder; specifically, we leverage extracted image attributes to explicitly structure its sequential latent space according to different localized style characteristics. Our experiments on the Senticap and COCO datasets show the ability of our approach to generate accurate captions with diversity in styles that are grounded in the image.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Klein, Franz ; Mahajan, Shweta ; Roth, Stefan |
| Art des Eintrags: | Bibliographie |
| Titel: | Diverse Image Captioning with Grounded Style |
| Sprache: | Englisch |
| Publikationsjahr: | 28 September 2021 |
| Verlag: | Springer |
| Buchtitel: | Pattern Recognition |
| Reihe: | Lecture Notes in Computer Science |
| Band einer Reihe: | 13024 |
| Veranstaltungstitel: | 43rd German Conference on Pattern Recognition (GCPR 2021) |
| Veranstaltungsdatum: | 28.09.2021-01.10.2021 |
| DOI: | 10.1007/978-3-030-92659-5_27 |
| URL / URN: | https://link.springer.com/chapter/10.1007/978-3-030-92659-5_... |
| Kurzbeschreibung (Abstract): | Stylized image captioning as presented in prior work aims to generate captions that reflect characteristics beyond a factual description of the scene composition, such as sentiments. Such prior work relies on given sentiment identifiers, which are used to express a certain global style in the caption, e.g. positive or negative, however without taking into account the stylistic content of the visual scene. To address this shortcoming, we first analyze the limitations of current stylized captioning datasets and propose COCO attribute-based augmentations to obtain varied stylized captions from COCO annotations. Furthermore, we encode the stylized information in the latent space of a Variational Autoencoder; specifically, we leverage extracted image attributes to explicitly structure its sequential latent space according to different localized style characteristics. Our experiments on the Senticap and COCO datasets show the ability of our approach to generate accurate captions with diversity in styles that are grounded in the image. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Visuelle Inferenz |
| Hinterlegungsdatum: | 08 Mär 2022 07:57 |
| Letzte Änderung: | 08 Mär 2022 07:57 |
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