Text-Based Video Moment Retrieval Without Temporal Boundary Labels
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Solution Overview
Problem
Existing methods for text-based video moment retrieval in untrimmed video data rely on fully-supervised learning, which is time-consuming and expensive, and struggle with ambiguous temporal boundaries, making accurate annotation difficult.
Innovation Solution
A method that combines visual features of video segments with textual features of a query to generate a similarity matrix, using segment-attended sentence features to retrieve video moments without full supervision, employing neural networks for alignment scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fully-supervised learning is used for text-based video moment retrieval, then retrieval accuracy can be improved, but the process becomes extraordinarily time-consuming and expensive
Solution Approach 1:
The patent applies preliminary action by pre-extracting visual features from video segments and pre-computing textual features from queries before the actual retrieval process. This allows the system to perform efficient matching without requiring time-consuming fully-supervised training, as the feature extraction is prepared in advance and can be quickly matched during retrieval operations
Solution Approach 2:
The patent uses copying by creating a similarity matrix that copies and stores the relationship between visual features and textual features in a compressed format. This similarity matrix serves as a reference that can be quickly queried during retrieval without re-performing the complex feature matching calculations, thus reducing retrieval time while maintaining accuracy
2Measurement precision
If fully-supervised learning with annotated temporal boundaries is used, then retrieval accuracy improves, but the annotation process becomes difficult and time-consuming due to ambiguous temporal boundaries
Solution Approach 1:
The patent extracts the temporal boundary annotation requirement from the fully-supervised learning process. Instead of requiring precise annotated temporal boundaries for training, the system extracts visual features from video segments and matches them with textual features to infer temporal relationships, thereby eliminating the difficult and time-consuming annotation process while maintaining retrieval accuracy
Solution Approach 2:
The patent replaces the mechanical manual annotation process with an automated feature-based matching system. Instead of requiring human annotators to manually define temporal boundaries, the system uses visual feature extraction and similarity matrix computation to automatically identify relevant video moments, substituting the manual mechanical process with an automated computational approach
3Quantity of substance
If segment-attended sentence features are used to retrieve video moments, then the need for extensive training data is reduced, but the system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the video content into discrete video segments and the textual query into individual words or phrases. The system then computes similarities between each video segment and each textual unit, creating a fine-grained similarity matrix. This segmentation allows the system to handle complex retrieval tasks without requiring extensive training data, as the modular approach enables efficient combination of results from smaller units
Solution Approach 2:
The patent uses dimensionality change by transforming the retrieval problem from a high-dimensional space requiring extensive training data to a lower-dimensional similarity matrix that can be efficiently computed and queried. The similarity matrix compresses the relationships between visual and textual features into a compact structure that reduces the amount of training data needed while maintaining the system's ability to handle complex retrieval tasks
Data Source
AI summary
An electronic device obtains video content and a textual query associated with a video moment in the video content. The video content is divided video segments, and the textual query includes one or more words. Visual features are extracted for each video segment, and textual features are extracted for each word. The visual features and the textual features are combined to generate a similarity matrix in which each element represents a similarity level between a respective video segment and a respective word. Segment-attended sentence features are generated for the textual query based on the textual features and the similarity matrix. The segment-attended sentence features are combined with the visual features of the video segments to determine a plurality of alignment scores, which is used to retrieve a subset of the video content associated with the textual query to be retrieved from the video segments.