Mickael Stefas


2016

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Benchmarking multimedia technologies with the CAMOMILE platform: the case of Multimodal Person Discovery at MediaEval 2015
Johann Poignant | Hervé Bredin | Claude Barras | Mickael Stefas | Pierrick Bruneau | Thomas Tamisier
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In this paper, we claim that the CAMOMILE collaborative annotation platform (developed in the framework of the eponymous CHIST-ERA project) eases the organization of multimedia technology benchmarks, automating most of the campaign technical workflow and enabling collaborative (hence faster and cheaper) annotation of the evaluation data. This is demonstrated through the successful organization of a new multimedia task at MediaEval 2015, Multimodal Person Discovery in Broadcast TV.

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The CAMOMILE Collaborative Annotation Platform for Multi-modal, Multi-lingual and Multi-media Documents
Johann Poignant | Mateusz Budnik | Hervé Bredin | Claude Barras | Mickael Stefas | Pierrick Bruneau | Gilles Adda | Laurent Besacier | Hazim Ekenel | Gil Francopoulo | Javier Hernando | Joseph Mariani | Ramon Morros | Georges Quénot | Sophie Rosset | Thomas Tamisier
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In this paper, we describe the organization and the implementation of the CAMOMILE collaborative annotation framework for multimodal, multimedia, multilingual (3M) data. Given the versatile nature of the analysis which can be performed on 3M data, the structure of the server was kept intentionally simple in order to preserve its genericity, relying on standard Web technologies. Layers of annotations, defined as data associated to a media fragment from the corpus, are stored in a database and can be managed through standard interfaces with authentication. Interfaces tailored specifically to the needed task can then be developed in an agile way, relying on simple but reliable services for the management of the centralized annotations. We then present our implementation of an active learning scenario for person annotation in video, relying on the CAMOMILE server; during a dry run experiment, the manual annotation of 716 speech segments was thus propagated to 3504 labeled tracks. The code of the CAMOMILE framework is distributed in open source.