Few-Shot Video Classification Using Meta-Action Frame Weighting
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Solution Overview
Problem
The need for labeled training data poses a challenge in creating machine learning-based video classification models, making the creation of such models time-consuming and resource-intensive.
Innovation Solution
A method involving meta-action learning and reinforced image representation is employed to enrich video features using a meta-action bank, refine representations through single-value decomposition, and utilize reinforcement learning to determine the importance of frames, thereby enhancing the classification process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional machine learning-based video classification is used, then classification capability is achieved, but the need for labeled training data makes model creation time-consuming and resource-intensive
Solution Approach 1:
The system performs preliminary action by pre-defining a meta-action bank containing common video sub-actions and their temporal patterns before actual video classification. This pre-established knowledge base enables the model to classify videos with minimal labeled training data, as the meta-action bank provides prior structural information about action compositions.
Solution Approach 2:
The meta-action bank serves as an intermediary between raw video frames and final classification results. Instead of directly classifying entire videos which requires extensive labeled data, the system breaks down video classification into sub-action recognition using the meta-action bank as a mediator, significantly reducing the labeled training data requirement.
2Measurement precision
If reinforcement learning is used to determine frame importance, then classification accuracy is improved, but computational complexity increases
Solution Approach 1:
The system applies local quality by using reinforcement learning selectively only for determining the importance weight of individual frames, rather than applying complex computational methods to the entire video processing pipeline. This localized application of reinforcement learning improves frame importance determination accuracy while limiting computational complexity increase to specific critical steps.
Solution Approach 2:
The reinforcement learning component dynamically changes the importance parameter weights of video frames based on their relevance to the predicted action. By adjusting these parameters adaptively rather than using fixed weights, the system achieves higher classification accuracy without requiring fundamentally more complex computational infrastructure.
Data Source
AI summary
Methods and systems for video processing include enriching an input video feature from an input video frame set using a meta-action bank video sub-actions to generate enriched features. Reinforced image representation is performed using reinforcement learning to compare support image frames and query image frames and determine an importance of the input video frame. A classification is performed on the input video frame based on the importance and the enriched features to generate a label. An action is performed responsive to the generated label.