Deep Explorer
Speeding up research time
How can I recognize people and objects more quickly and establish references between them?
use case: Improving video recommendations
the challenge
Every video platform aims to offer its users the best and most individual customer experience in order to stand out from the multitude of competitors. They want to increase the time spent on the site and the rate of interaction with advertisements. These goals can only be achieved with an accurate and reliable recommendation system that goes beyond video titles or video descriptions. Can DeepVA help me to provide better recommendations?
the solution
The DeepVA Deep Explorer enriches a media archive with demographic and factual knowledge and enables a recommendation system via an extensive AI-based analysis of the visual material. This happens with regard to relationships and similarities between assets, which in terms of its quality clearly goes beyond the pure interaction of the user via time or frequency. The combination of user data and metadata from the latest AI technology makes this system so precise and reliable.
The system can be used on-premise or in the cloud. If it is to be part of a workflow, integration is required. Via our RESTful API, it can be easily integrated in any existing system or workflow. Data protection requirements usually play a major role in this decision and should be considered.
What results can be obtained?
Better products for users: provide context and details
faster data acquisition
COST REDUCTION
faster labelling
Improving video recommendations
Recognizes the relationships between people or objects, represents them visually, and thus helps to stay one step ahead of the competition.
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Related use cases
How can I recognize people and objects more quickly and establish references between them?
How can I efficiently find what I’m really looking for in my visual media material?
How can I automatically create thumbnails for the media library from my material?
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Designed and engineered in Germany.
Aiconix GmbH
Wannerstraße 19
79106 Freiburg
Germany
The project has indirectly received funding from the European Commission’s Horizon 2020 Framework Programme through the STADIEM project (Grant Agreement 957321)
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