Abstract
One of the processing tasks for large multimodal data streams is automatic image description (image classification, object segmentation and classification). Although the number and the diversity of image datasets is constantly expanding, still there is a huge demand for more datasets in terms of variety of domains and object classes covered. The goal of the project Multilingual Image Corpus (MIC 21) is to provide a large image dataset with annotated objects and object descriptions in 24 languages. The Multilingual Image Corpus consists of an Ontology of visual objects (based on WordNet) and a collection of thematically related images whose objects are annotated with segmentation masks and labels describing the ontology classes. The dataset is designed both for image classification and object detection and for semantic segmentation. The main contributions of our work are: a) the provision of large collection of high quality copyright-free images; b) the formulation of the Ontology of visual objects based on WordNet noun hierarchies; c) the precise manual correction of automatic object segmentation within the images and the annotation of object classes; and d) the association of objects and images with extended multilingual descriptions based on WordNet inner- and interlingual relations. The dataset can be used also for multilingual image caption generation, image-to-text alignment and automatic question answering for images and videos.- Anthology ID:
- 2022.lrec-1.162
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 1509–1518
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.162
- DOI:
- Cite (ACL):
- Svetla Koeva, Ivelina Stoyanova, and Jordan Kralev. 2022. Multilingual Image Corpus – Towards a Multimodal and Multilingual Dataset. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 1509–1518, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Multilingual Image Corpus – Towards a Multimodal and Multilingual Dataset (Koeva et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.lrec-1.162.pdf
- Data
- MS COCO