Graph Convolution Networks for Video Grounding Accuracy
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Solution Overview
Problem
Current video grounding techniques rank video segments individually, failing to effectively capture the relationships between segments, which limits the accuracy of identifying matching segments for natural language queries.
Innovation Solution
The use of a graph convolution network (GCN) to rank video proposals by identifying relationships between them, constructing a graph with nodes representing fused visual and query features and edges based on similarity, allowing for global and comprehensive video grounding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If proposals are ranked individually using traditional methods, then the ranking process is simple and fast, but the accuracy of identifying matching video segments is limited
Solution Approach 1:
The patent transitions from individual proposal ranking to a graph-based structure where proposals are nodes and relationships are edges. This dimensional change allows the system to capture temporal and contextual relationships between proposals, significantly improving grounding accuracy by considering the global structure of video segments rather than evaluating each proposal in isolation.
Solution Approach 2:
The patent segments the video into multiple proposals and represents them as discrete nodes in a graph structure. This segmentation enables the application of graph convolution operations to capture relationships between adjacent and related segments, improving the system's ability to understand temporal dynamics and contextual relationships in video data.
2Measurement precision
If relationships between proposals are explicitly modeled using graph convolution networks, then the accuracy of video grounding is significantly boosted, but the computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary actions by pre-computing the graph structure, node features, and edge relationships before the actual ranking process. By preparing the graph convolution network with pre-extracted features and established relationships, the system reduces computational overhead during inference, thereby mitigating the time loss despite the increased complexity of relationship modeling.
3Loss of information
If a graph structure is constructed to represent relationships between proposals, then temporal relationships are explicitly captured, but the device complexity and computational resources required increase
Solution Approach 1:
The patent introduces a graph structure as an intermediary representation between raw video proposals and the final ranking output. This intermediary graph structure serves as a mediator that explicitly models temporal and contextual relationships, allowing the system to capture information that would be lost in individual proposal evaluation while providing a structured framework for applying graph convolution operations.
Data Source
AI summary
Method and apparatus that includes receiving a query describing an aspect in a video, the video including a plurality of frames, identifying multiple proposals that potentially correspond to the query where each of the proposals includes a subset of the plurality of frames, ranking the proposals using a graph convolution network that identifies relationships between the proposals, and selecting, based on the ranking, one of the proposals as a video segment that correlates to the query.