VideoContentSearchMultimodalTopicTrainingFeatures

AI Overview😉

  • The potential purpose of this module is to analyze and understand the content of videos, particularly the visual and audio elements, to improve search results. It appears to be focused on multimodal topic modeling, which means it's trying to identify the main topics or themes in a video by analyzing both visual and audio cues.
  • This module could impact search results by allowing Google to better understand the content of videos and return more relevant results for search queries. For example, if a user searches for "cooking recipes", this module could help Google identify videos that show cooking techniques and recipes, even if the video title or description doesn't explicitly mention those keywords. This could lead to more accurate and relevant search results.
  • To be more favorable for this function, a website could optimize its video content by: Providing high-quality, descriptive thumbnails that accurately represent the video content Including relevant keywords and descriptions in the video title, description, and tags Ensuring that the video's audio and visual elements are clear and easy to understand Using schema markup or other metadata to provide additional context about the video's content Creating videos that are well-structured and easy to follow, with clear topics and themes

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

Multimodal features for a single generated topic used to build training data.

Attributes

  • maxFrameSimilarityInterval (type: GoogleApi.ContentWarehouse.V1.Model.VideoContentSearchFrameSimilarityInterval.t, default: nil) - The similarity info for the frame with maximum similarity to the topic in its visual interval. The repeated similarity field in this proto has a single value corresponding to the maximum similarity. This similarity score is used to filter and pick the training data examples.
  • normalizedTopic (type: String.t, default: nil) - The topic/query normalized for Navboost and QBST lookups as well as fetching of the Rankembed nearest neighbors.
  • qbstTermsOverlapFeatures (type: GoogleApi.ContentWarehouse.V1.Model.VideoContentSearchQbstTermsOverlapFeatures.t, default: nil) - QBST terms overlap features for a candidate query.
  • rankembedNearestNeighborsFeatures (type: GoogleApi.ContentWarehouse.V1.Model.VideoContentSearchRankEmbedNearestNeighborsFeatures.t, default: nil) - Rankembed similarity features for a candidate nearest neighbor rankembed query.
  • saftEntityInfo (type: GoogleApi.ContentWarehouse.V1.Model.VideoContentSearchSaftEntityInfo.t, default: nil) - The information about the saft entity annotation for this topic.
  • topicDenseVector (type: list(number()), default: nil) - Raw float feature vector of the topic's co-text embedding representation in the Starburst space.

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.VideoContentSearchMultimodalTopicTrainingFeatures{
    maxFrameSimilarityInterval:
      GoogleApi.ContentWarehouse.V1.Model.VideoContentSearchFrameSimilarityInterval.t()
      | nil,
    normalizedTopic: String.t() | nil,
    qbstTermsOverlapFeatures:
      GoogleApi.ContentWarehouse.V1.Model.VideoContentSearchQbstTermsOverlapFeatures.t()
      | nil,
    rankembedNearestNeighborsFeatures:
      GoogleApi.ContentWarehouse.V1.Model.VideoContentSearchRankEmbedNearestNeighborsFeatures.t()
      | nil,
    saftEntityInfo:
      GoogleApi.ContentWarehouse.V1.Model.VideoContentSearchSaftEntityInfo.t()
      | nil,
    topicDenseVector: [number()] | nil
  }

Functions

Link to this function

decode(value, options)

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

Unwrap a decoded JSON object into its complex fields.