Update README.md

README.md

@@ -18,5 +18,21 @@ All the images have been annotated by six annotators to contain one of three cla
 Each of the six annotators have labelled all the images (that is, six individual annotations are provided for each image), which allows for analyzing how inter-annotator disagreement can affect the performance of machine learning models.
 Cross-validation splits and date-based splits are provided in the [jambo_splits_public.csv](jambo_splits_public.csv) file. Check out the starter notebook [howto_jambo.ipynb](howto_jambo.ipynb) to get started.
 
-For more information about the dataset and baseline models, please see the paper to be presented the ECCV 2024 Computer Vision for Ecology (CV4E) Workshop: [Underwater Uncertainty: A Multi-Annotator Image Dataset for Benthic Habitat
-Classification](https://vbn.aau.dk/ws/portalfiles/portal/738329849/JAMBO_ECCV_vbn.pdf)
+For more information about the dataset and baseline models, please see the paper presented the ECCV 2024 Computer Vision for Ecology (CV4E) Workshop:
+[Underwater Uncertainty: A Multi-Annotator Image Dataset for Benthic Habitat
+Classification](https://vbn.aau.dk/ws/portalfiles/portal/738329849/JAMBO_ECCV_vbn.pdf) (Springer link [here](doi.org/10.1007/978-3-031-92387-6_6))
+
+Citation:
+```
+@InProceedings{jambo_dataset,
+  author="Humblot-Renaux, Galadrielle and Johansen, Anders Skaarup and Schmidt, Jonathan Eichild and Irlind, Amanda Frederikke and Madsen, Niels and Moeslund, Thomas B. and Pedersen, Malte",
+  title="Underwater Uncertainty: A Multi-annotator Image Dataset for Benthic Habitat Classification",
+  booktitle="Computer Vision -- ECCV 2024 Workshops",
+  year="2025",
+  publisher="Springer Nature Switzerland",
+  address="Cham",
+  pages="87--104",
+  isbn="978-3-031-92387-6"
+  doi="10.1007/978-3-031-92387-6_6"
+}
+```