Video archives hold visual content, speech, sound, subtitles and events that are difficult to express in fixed tags. A program title or an automatically generated transcript rarely tells a user where a specific conversation, person or scene appears.
We built a multimodal pipeline: analyzing footage → identifying scenes and topics → describing relevant content → connecting related information → searching in natural language. The challenge is to connect what is seen and heard with its place in the recording, so users can find the relevant moment.
Recovering Meaning Across Video, Audio and Time
- Visual and spoken content provide different evidence
Objects and scenes appear in video, while names, explanations and key remarks may be found in audio or subtitles. Neither source is complete by itself. - Fixed-duration segments can split an event
A scene or conversation may cross arbitrary time boundaries, making its meaning difficult to index or summarize accurately. - Flat tags lose relationships
Knowing that a person, place and event occur in a program is not enough to show how they relate to one another. - Retrieval must identify the relevant moment
Users often need a particular quotation, event or scene, not an entire program-length asset.
A Multimodal Pipeline for Scene-Level Retrieval
- Analyze complementary media streams
We combined video frames, audio, subtitles and textual information to identify people, objects, places, conversations, topics and events. - Segment around changes in meaning
We organized footage using changes in scenes, subjects, conversations or events instead of relying only on fixed time intervals. - Generate metadata for each meaningful segment
We created keywords, descriptions, summaries, notable remarks and representative images linked to the relevant part of the footage. - Represent relationships between entities
We structured associations among people, organizations, events, locations and programs so related material can be explored beyond keyword search. - Support natural-language, scene-focused search
We matched a request to relevant content and returned the portion of the video that addressed it rather than only a program-level result. - Prepare metadata for multilingual reuse
We organized the extracted knowledge so translation, summaries and downstream descriptive content can be supported where required.
Key Engineering Decisions
- Fuse modalities without discarding their differences
Speech, imagery and subtitles contribute different signals; analysis must preserve enough context to interpret them together. - Make the scene the unit of retrieval
The useful output is a relevant passage or event within a recording, not merely a matching video filename. - Connect entities and content over time
Relationships support exploration of people and events while the underlying scene context remains available.
From Flat Video Tags to Scene-Level Knowledge
We built scene-level retrieval from multimodal evidence, meaningful segments and entity relationships. Linking descriptions to their location in the footage made the relevant scene, rather than only the program, the unit of search.