Content Adaptation, Personalisation and Fine-Grained Retrieval: Applying AI to Support Engagement with and Reuse of Archival Content at Scale
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The document discusses using AI to support reuse of archival audiovisual content at scale. It describes the ReTV project which aims to automatically adapt broadcaster content for digital platforms through techniques like video summarization. The project also explores personalizing access to archives through a chatbot and making content more discoverable in media asset management systems through generous interfaces that allow retrieval by concepts. The goal is to generate more value from archival collections by bringing content to audiences on digital channels.
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Content Adaptation, Personalisation and Fine-Grained Retrieval: Applying AI to Support Engagement with and Reuse of Archival Content at Scale
- 1. retv-project@ReTVproject@ReTV_EUretv-project.eu Content Adaptation, Personalisation and Fine- Grained Retrieval: Applying AI to Support Engagement with and Reuse of Archival Content at Scale Rasa Bocyte, NISV Johan Oomen, NISV 23-02-2020 ICAART 2020 1
- 2. retv-project@ReTVproject@ReTV_EUretv-project.eu ReTV: Reuse of Audiovisual Archives Fundamental need: - Generate value out of archival AV content through reuse; nothing good comes out of just keeping the content locked in your digital basement - Reusing content with impact - bringing content to where your audiences are - Archives need to be visible and present on digital channels 2
- 3. retv-project@ReTVproject@ReTV_EUretv-project.eu 3 Manual curation Discovering the right content Publication across multiple channels Different content for different platforms Adapting broadcaster content for digital channels Video editing Challenges for large- scale archival collections
- 4. retv-project@ReTVproject@ReTV_EUretv-project.eu Reuse in Audiovisual Archives 4 This is where AI steps in! - Understanding AV content for meaningful curation - Making AV content discoverable on a granular level - Automatically adapting broadcasted content and publishing it on digital platforms
- 7. retv-project@ReTVproject@ReTV_EUretv-project.eu ReTV Use Cases Audiovisual Content Reuse 1. Broadcaster Content Adaptation - Reusing Videos on Social Media 1. Personalised Access to the Archive - Personalised Content Delivery via a Chatbot 1. AV Content Discovery & Retrieval - Support Reuse through Generous Media Asset Management Systems 7
- 8. retv-project@ReTVproject@ReTV_EUretv-project.eu Broadcaster Content Adaptation for Social Media Video Summarisation 8 - Fast-paced consumption - no time for full-length programmes - Target distribution platforms & devices have varying requirements (e.g. optimal duration that is not suitable for full-length broadcaster content)
- 9. retv-project@ReTVproject@ReTV_EUretv-project.eu Broadcaster Content Adaptation for Social Media Video Summarisation Video summarisation: - a visual summary that encapsulates the flow of the story and the essential parts of the full-length video - Teaser into the archive 9
- 10. retv-project@ReTVproject@ReTV_EUretv-project.eu Broadcaster Content Adaptation for Social Media Video Summarisation Example - Original video (1’38’’) ⇒ 14’’ summary - Fully automatic summary generation 10
- 11. retv-project@ReTVproject@ReTV_EUretv-project.eu 11 Broadcaster Content Adaptation for Social Media Video Summarisation
- 12. retv-project@ReTVproject@ReTV_EUretv-project.eu Requirements: - Different training for different content (e.g. separate trained models can be used for different TV programmes) - Action-packed shots (no talking heads shots) - BUT no spoilers - Audio track analysis to supplement visual analysis 12 Broadcaster Content Adaptation for Social Media Video Summarisation
- 14. retv-project@ReTVproject@ReTV_EUretv-project.eu Personalisation for Archives Chatbot - Exploring what personalisation means for archives - How to bring content to your audiences and keep them interested? Possibilities for long-term engagement via personalised content Archival Chatbot - Bringing content directly to audiences via channels convenient to them - Personalisation to increase the reuse of AV content - get that niche content out there! 14
- 16. retv-project@ReTVproject@ReTV_EUretv-project.eu Personalised Access to the Archive Chatbot Lessons learned - rethinking personalisation - great tool for fostering more personal engagement with the past - expand what people are interested instead of narrowing their interests down - not only content that you are already familiar/interested in but helping to discover something new - make personalisation transparent - let users understand and be in control (through direct interaction via chatbot) 16
- 17. retv-project@ReTVproject@ReTV_EUretv-project.eu Personalised Access to the Archive Chatbot Next iteration: - chatbot as a way to showcase how personalisation works - “Send me a GIF” feature - text to video matching 17
- 18. retv-project@ReTVproject@ReTV_EUretv-project.eu AV Content Discovery & Retrieval Generous Media Asset Management Systems - boost the reuse of content through MAMs by producers, artists, documentary makers, amateur creators - retrieval constrained by high-level descriptions - you need to know what you are looking for - retrieval through a generous interface for AV content 18
- 20. retv-project@ReTVproject@ReTV_EUretv-project.eu AV Content Discovery & Retrieval Generous Media Asset Management Systems Why go generous? - more intuitive, serendipitous search - discovery of rarely used content - overview at a glance on an item or collection level - granular access to specific segments and concepts 20
- 22. retv-project@ReTVproject@ReTV_EUretv-project.eu 22 AV Content Discovery & Retrieval Individual Shot Visualisation
- 23. retv-project@ReTVproject@ReTV_EUretv-project.eu AV Content Discovery & Retrieval Retrieval by Concepts 23 Sample video frame Top detected concepts Web interface: http://multimedia2.iti.gr/onlinevideoanalysis/service/start.html
- 24. retv-project@ReTVproject@ReTV_EUretv-project.eu AV Content Discovery & Retrieval Generous Media Asset Management Systems Challenges for being generous with AV content - meaningful concept annotation - scalability - putting all the pieces together - video analysis, speech to text, face recognition, etc. 24
- 25. retv-project@ReTVproject@ReTV_EUretv-project.eu 25 This work was supported by the EUs Horizon 2020 research and innovation programme under grant agreement H2020-780656 ReTV Rasa Bocyte, NISV rbocyte@beeldengeluid.nl @rasa_bocyte Johan Oomen, NISV joomen@beeldengeluid.nl @johanoomen
Notes de l'éditeur
- add partners
- use cases
- REST service available (for integration in applications / CMSs)
- Levuro screenshot
- Audio - not lose contextual info to contextualise the story
- So we wanted to see how this translates to archival collections and the mission that CHIs have.
- MAM interface
- temporal fragmentation of a video (subshots/shots/scenes)
- fragment annotation with concept labels that describe them (>6000 labels), text to video matching.
- comprehensive understanding