Zero-shot Event Detection via Semantic Embedding Space
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current automated systems for event detection in videos require extensive training on labeled examples and struggle with zero-shot event detection, where no positive exemplar videos are available, making it challenging to retrieve relevant videos for arbitrary events from large volumes of consumer-generated content like YouTube.
Innovation Solution
A zero-shot content detection system that uses semantic embedding techniques to map word-based document descriptions and multimedia content into a multi-dimensional space, allowing for the automatic detection of relevant concepts and events without training examples, by embedding words representative of content detection actions into a semantic space and determining relevant concepts based on their location and similarity functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine-learning-trained classifiers are used for event detection, then event detection accuracy is improved, but extensive training data and time are required
Solution Approach 1:
The system performs preliminary embedding of event descriptions and video content into a semantic space before actual event detection queries are executed. This pre-computation of semantic representations allows the system to perform zero-shot event detection without requiring training time for each new event type, as the semantic space is already constructed and ready for querying.
2Reliability
If machine-learning-trained classifiers are used for event detection, then event detection capability is improved, but extensive labeled training examples are required
Solution Approach 1:
The patent introduces a semantic space as an intermediary between event descriptions and video content. Instead of directly training classifiers on labeled video examples, the system embeds both event descriptions and video content into the semantic space using distributional semantic models. This intermediary representation allows the system to perform event detection by comparing semantic similarities, eliminating the need for extensive labeled training data.
3Measurement precision
If traditional content detection methods are used, then detection accuracy for known events is improved, but adaptability to new events is reduced
Solution Approach 1:
The patent creates a universal semantic space that can represent both known and new event types using the same embedding framework. The distributional semantic model learns general semantic relationships from large corpora, enabling the system to detect both familiar and novel events by comparing their semantic representations. This universal representation approach allows the system to maintain detection accuracy while being highly adaptable to new event types without retraining.
Data Source
AI summary
Zero-shot content detection includes building/training a semantic space by embedding word-based document descriptions of a plurality of documents into a multi-dimensional space using a semantic embedding technique; detecting a plurality of features in the multimodal content by applying feature detection algorithms to the multimodal content; determining respective word-based concept descriptions for concepts identified in the multimodal content using the detected features; embedding the respective word-based concept descriptions into the semantic space; and in response to a content detection action, (i) embedding/mapping words representative of the content detection action into the semantic space, (ii) automatically determining, without the use of training examples, concepts in the semantic space relevant to the content detection action based on the embedded words, and (iii) identifying portions of the multimodal content responsive to the content detection action based on the concepts in the semantic space determined to be relevant to the content detection action.