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MovieNet Scene Classification

Classify movie scenes by type, place, and cinematic attributes. Annotators label scenes with location, time of day, weather, and narrative function.

Labels:outdoornatureurbanpeopleanimal+−

Configuration Fileconfig.yaml

# MovieNet Scene Classification Configuration
# Based on Qian et al., ECCV 2020
# Task: Classify movie scenes by various attributes

annotation_task_name: "MovieNet Scene Classification"
task_dir: "."

data_files:
  - data.json
item_properties:
  id_key: "id"
  text_key: "video_url"

output_annotation_dir: "annotation_output/"
output_annotation_format: "json"

annotation_schemes:
  - name: "place_type"
    description: "What type of place/location is this scene set in?"
    annotation_type: radio
    labels:
      - "Indoor - Home/Apartment"
      - "Indoor - Office/Workplace"
      - "Indoor - Public (restaurant, store, etc.)"
      - "Indoor - Vehicle"
      - "Outdoor - Urban/City"
      - "Outdoor - Nature/Rural"
      - "Outdoor - Road/Street"
      - "Mixed/Transitional"

  - name: "time_of_day"
    description: "What time of day is depicted?"
    annotation_type: radio
    labels:
      - "Day - Morning"
      - "Day - Afternoon"
      - "Day - Evening/Dusk"
      - "Night"
      - "Unclear/Mixed"

  - name: "weather"
    description: "What is the weather/lighting condition?"
    annotation_type: radio
    labels:
      - "Clear/Sunny"
      - "Cloudy/Overcast"
      - "Rainy"
      - "Snowy"
      - "Foggy/Misty"
      - "Indoor (N/A)"
      - "Unclear"

  - name: "scene_function"
    description: "What narrative function does this scene serve?"
    annotation_type: multiselect
    labels:
      - "Exposition (introduces information)"
      - "Action/Chase"
      - "Dialogue/Conversation"
      - "Emotional/Dramatic"
      - "Comedic"
      - "Romantic"
      - "Suspense/Tension"
      - "Flashback/Dream"
      - "Montage/Transition"

  - name: "shot_scale"
    description: "What is the predominant shot scale?"
    annotation_type: radio
    labels:
      - "Extreme Close-up"
      - "Close-up"
      - "Medium Shot"
      - "Full Shot"
      - "Long Shot"
      - "Extreme Long Shot"
      - "Mixed/Varied"

allow_all_users: true
instances_per_annotator: 50
annotation_per_instance: 2

annotation_instructions: |
  ## Movie Scene Classification Task

  Classify movie scenes by their visual and narrative attributes.

  ### Attributes to Label:

  **Place Type**: Where does the scene take place?

  **Time of Day**: When does the scene occur?

  **Weather**: What's the lighting/weather condition?

  **Scene Function**: What purpose does this scene serve narratively?

  **Shot Scale**: What's the typical camera distance?

  ### Tips:
  - Consider the OVERALL scene, not just one shot
  - Scene function can have multiple labels
  - If unsure, choose the most prominent/frequent option

Sample Datasample-data.json

[
  {
    "id": "movienet_001",
    "video_url": "https://example.com/videos/movie_scene_office.mp4",
    "movie": "Sample Movie",
    "scene_number": 1
  },
  {
    "id": "movienet_002",
    "video_url": "https://example.com/videos/movie_scene_outdoor.mp4",
    "movie": "Sample Movie",
    "scene_number": 2
  }
]

Get This Design

View on GitHub

Clone or download from the repository

Quick start:

git clone https://github.com/davidjurgens/potato-showcase.git
cd potato-showcase/video/scene-understanding/movienet-scene-classification
potato start config.yaml

Details

Annotation Types

multiselectradio

Domain

Computer VisionFilm Studies

Use Cases

Scene ClassificationMovie UnderstandingContent Analysis

Tags

videomoviesceneclassificationmovienetcinematic

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