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An application that builds context-dependent knowledge base with video label dataset.

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Video Knowledge Builder

This is an application that takes JSONLines formatted video label dataset as input and builds context dependent knowledge base.

Input format

Input data must be JSONLines that contains multiple JSON objects in one of the formats below.

Object

  • {"type": "object", "class": "person", "label": "Person A", "seconds": 15.0, "coordinates": [100, 200, 20, 30]}
  • {"type": "object", "id": "person_ross_geller", "class": "person", "label": "Ross Geller", "seconds": 15.0, "coordinates": [100, 200, 20, 30]}

Behavior

  • {"type": "behavior", "class": "stand", "seconds": 15.0, "object": Object}

Emotion

  • {"type": "emotion", "class": "happy", "seconds": 15.0, "object": Object}

Relation

  • {"type": "relation", "class": RelationalClass, "subclass": "wear", "seconds": 15.0, "source": Object, "target": Object}

Location

  • {"type": "location", "class": "central_perk", "seconds": 15.0}

Sound

  • {"type": "sound", "class": "glass_crashing", "seconds": 15.0}

Object

  • {"id": "person_ross_geller"} or
  • {"coordinates": [100, 200, 20, 30]}

RelationalClass

  • "behavior" for subclass "wear", "hold", "hug", …
  • "emotion" for subclass "love", "like", …
  • "position" for subclass "above", "below", "next_to", …
  • "social" for subclass "son_of", "father_of", "lover_of", "friend_of", …

How to use

main.py takes input data as standard input, and prints result knowledge base as standard output as a JSONLines formatted string. For example:

video-knowledge-builder $ python main.py < test_input.jsonlines > output.jsonlines

The shell command above will provide the contents of test_input.jsonlines as input for the application and save the result in a file output.jsonlines.

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An application that builds context-dependent knowledge base with video label dataset.

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  • Python 100.0%