Objective
CLASS is a basic research project focused on developing a specific cognitive ability for use in intelligent content analysis: the automatic discovery of content categories and attributes from unstructured content streams. More specifically, it will study object recognition and scene analysis in images, video, and accompanying text streams. Current visual recognition methods either work at unsatisfyingly low semantic levels, or require large amounts of manually labelled data for training. Autonomous learning will make them more adaptive and allow more general classes and much larger and more varied data sets to be handled. This will be useful in many applications of cognitive systems and intelligent agents that handle streams of sensed data.
We will study both fully autonomous and semi-supervised methods at three levels of abstraction: new individuals (specific people, objects, scenes, actions); new object classes and attributes; and hierarchical categories and relations between entities. The work will combine robust computer vision based image descriptors, machine learning based latent structure models, and advanced textual summarization techniques. The potential applications of the basic research results will be illustrated by three proof-of-concept demonstrators: an Image Interrogator that interactively answers simple user-defined queries about image content; a Video Commentator that automatically creates textual descriptions of the action and content of situation comedy videos for visually impaired users; and a News Digester that combines television news stories with captions from several sources to create a textual and visual digest of them.
The CLASS consortium is interdisciplinary, combining five leading European research teams in visual recognition, text understanding and summarization, and machine learning.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences artificial intelligence computer vision
- natural sciences computer and information sciences artificial intelligence machine learning
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Coordinator
78153 LE CHESNAY
France
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.