VideoContentSearchMultimodalTopicFeatures

AI Overview😉

  • The potential purpose of this module is to analyze and understand video content by generating topics and identifying relevant frames, timestamps, and queries. It seems to be a multimodal topic modeling approach that combines video and text features to better comprehend the video's content.
  • This module could impact search results by providing more accurate and relevant video content to users. For example, if a user searches for a specific topic, this module could help identify videos that discuss that topic and provide timestamps for when the topic is mentioned. This could lead to more precise and efficient search results.
  • A website may change things to be more favorable for this function by providing high-quality, descriptive, and accurate video metadata, such as titles, descriptions, and timestamps. Additionally, optimizing video content to be more discoverable by search engines, using relevant keywords and tags, could also improve the functionality of this module. Furthermore, providing a clear and concise structure for video content, such as chapters or sections, could help the module better understand the video's topic and relevance.

Interesting Module? Vote 👇

Voting helps other researchers find interesting modules.

Current Votes: 0

GoogleApi.ContentWarehouse.V1.Model.VideoContentSearchMultimodalTopicFeatures (google_api_content_warehouse v0.4.0)

Multimodal features for a single generated topic. Next ID: 8

Attributes

  • frameSimilarityInterval (type: list(GoogleApi.ContentWarehouse.V1.Model.VideoContentSearchFrameSimilarityInterval.t), default: nil) - The list of frame sequence similarities to this topic. The list of frames are picked to be around the topic timestamp. The set of frames selected are thresholded at a value to ensure the selected frame intervals are similar to the query.
  • generativeTopicPredictionFeatures (type: list(GoogleApi.ContentWarehouse.V1.Model.VideoContentSearchGenerativeTopicPredictionFeatures.t), default: nil) - The inference results from the prediction services that generate the topics.
  • navboostAnchorFeatures (type: GoogleApi.ContentWarehouse.V1.Model.VideoContentSearchNavboostAnchorFeatures.t, default: nil) - Features related to queries generated using document navboost data with timed anchors. Only populated if the query was generated using this approach.
  • topic (type: String.t, default: nil) - The text of the generated topic.
  • topicEndMs (type: String.t, default: nil) - End time of the topic.
  • topicStartMs (type: String.t, default: nil) - Start time of the topic.
  • videoQuerySource (type: String.t, default: nil) - How the query was generated.

Summary

Types

t()

Functions

decode(value, options)

Unwrap a decoded JSON object into its complex fields.

Types

Link to this type

t()

@type t() ::
  %GoogleApi.ContentWarehouse.V1.Model.VideoContentSearchMultimodalTopicFeatures{
    frameSimilarityInterval:
      [
        GoogleApi.ContentWarehouse.V1.Model.VideoContentSearchFrameSimilarityInterval.t()
      ]
      | nil,
    generativeTopicPredictionFeatures:
      [
        GoogleApi.ContentWarehouse.V1.Model.VideoContentSearchGenerativeTopicPredictionFeatures.t()
      ]
      | nil,
    navboostAnchorFeatures:
      GoogleApi.ContentWarehouse.V1.Model.VideoContentSearchNavboostAnchorFeatures.t()
      | nil,
    topic: String.t() | nil,
    topicEndMs: String.t() | nil,
    topicStartMs: String.t() | nil,
    videoQuerySource: String.t() | nil
  }

Functions

Link to this function

decode(value, options)

@spec decode(struct(), keyword()) :: struct()

Unwrap a decoded JSON object into its complex fields.