Graph Convolutional Network for Few-Shot Temporal Action Localization
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Solution Overview
Problem
Conventional few-shot temporal action localization systems fail to leverage relationships between action exemplars, leading to suboptimal accuracy and precision in classifying actions within untrimmed videos, as they independently compare proposed features with each exemplar without considering the relationships between them.
Innovation Solution
The system employs a graph convolutional network to model a support set of temporal action classifications as a graph, where nodes represent actions and edges denote similarities between them, allowing for convolution to pass messages and output matching scores that indicate the level of match between the action classifications and the action to be classified.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional few-shot temporal action localization systems independently compare proposed features with each exemplar, then the system complexity is low, but the accuracy and precision of action classification deteriorates
Solution Approach 1:
The patent merges the independent exemplar comparisons into a unified graph convolutional framework where all exemplars are interconnected through similarity edges. This allows the system to simultaneously consider relationships between multiple exemplars rather than processing them independently, thereby improving classification accuracy while maintaining computational feasibility through structured aggregation.
Solution Approach 2:
The patent introduces a graph convolutional network as an intermediary between the input features and classification output. This intermediary processes the relationships between exemplars through graph convolutions, transforming the raw similarity comparisons into refined matching scores that capture intra-support-set relationships without requiring the system to directly manage complex inter-exemplar interactions.
2Measurement precision
If the system models support set as a graph with nodes and edges representing actions and similarities, then the accuracy of temporal action localization improves, but the computational complexity increases
Solution Approach 1:
The patent segments the graph convolution process into distinct computational stages: constructing the similarity graph from support set exemplars, performing graph convolutions to propagate information through the graph structure, and generating matching scores from the convolved features. This segmentation allows each stage to be optimized independently and facilitates efficient implementation of the otherwise complex graph-based approach.
Solution Approach 2:
The patent applies partial graph convolution by selectively convolving only the relevant portions of the graph structure that contain meaningful relationships between exemplars. Rather than processing the entire graph uniformly, the method focuses computational resources on the most informative subgraphs and similarity relationships, thereby achieving high accuracy without proportionally increasing overall computational complexity.
3Measurement precision
If the system uses graph convolution to pass messages between nodes, then the precision of matching scores improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary construction of the similarity graph and pre-computation of edge weights based on exemplar relationships before the actual graph convolution process. By pre-organizing the graph structure and similarity metrics in advance, the system reduces the computational burden during the message-passing phase, allowing graph convolutions to execute more efficiently while still achieving high precision in matching scores.
Data Source
AI summary
Systems and techniques that facilitate few-shot temporal action localization based on graph convolutional networks are provided. In one or more embodiments, a graph component can generate a graph that models a support set of temporal action classifications. Nodes of the graph can correspond to respective temporal action classifications in the support set. Edges of the graph can correspond to similarities between the respective temporal action classifications. In various embodiments, a convolution component can perform a convolution on the graph, such that the nodes of the graph output respective matching scores indicating levels of match between the respective temporal action classifications and an action to be classified. In various embodiments, an instantiation component can input into the nodes respective input vectors based on a proposed feature vector representing the action to be classified. In various cases, the respective temporal action classifications can correspond to respective example feature vectors, and the respective input vectors can be concatenations of the respective example feature vectors and the proposed feature vector.