Weakly Supervised Video Action Localization via Dual-Model Architecture
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Solution Overview
Problem
Existing machine-learning models for video localization are costly and time-consuming to train due to the need for human-labeled video segment data, and they are prone to context errors by over-relying on contextual information rather than action features.
Innovation Solution
A video localization system that uses a classification model and an actionness model, trained with weak supervision using video-level action class labels, to localize actions in videos by reducing reliance on contextual information and focusing on general action presence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine-learning models are trained using hand-labeled video segment data, then the models can identify actions with detailed segment-level precision, but the training process becomes extremely time-consuming and expensive
Solution Approach 1:
The patent uses weakly-labeled video-level action labels instead of expensive hand-labeled segment-level annotations. These inexpensive labels serve as training data for the initial model, which then generates pseudo-labels for further training, eliminating the need for costly manual segment annotation while still achieving effective action localization
Solution Approach 2:
The patent introduces an intermediary actionness model that generates actionness scores for video segments. This intermediary component bridges the gap between weak video-level labels and the need for segment-level localization, enabling the system to learn from inexpensive labels while achieving precise action detection
2Adaptability or versatility
If conventional models rely heavily on contextual information for action identification, then the models can leverage available scene information, but they become prone to context errors by misidentifying actions based on environment rather than actual action features
Solution Approach 1:
The patent separates the action detection task into two independent components: an action class prediction model that identifies specific action types and an actionness model that detects general action presence. This segmentation allows each model to focus on specific features rather than relying on contextual cues, reducing context errors while maintaining adaptability
Solution Approach 2:
The patent applies different evaluation criteria to different aspects of action detection: the action class model evaluates specific action features while the actionness model evaluates general action presence. This local quality approach ensures that each component optimizes for its specific function, preventing context-driven misclassification
3Loss of information
If hand-labeled video segment data is used for training, then the training data provides detailed segment-level action information, but the cost and time required to generate sufficient training data increases significantly
Solution Approach 1:
The patent implements a self-service training approach where the model generates its own training labels. The initial model trained on weak video-level labels produces predictions that are used as pseudo-labels for further training, eliminating the need for manual segment annotation while preserving action segment information
Solution Approach 2:
The patent performs preliminary training with weak video-level labels before generating pseudo-labels. This preliminary action creates an initial model capable of producing reasonable predictions, which then serve as the foundation for generating high-quality training data without manual intervention
Data Source
AI summary
A video localization system localizes actions in videos based on a classification model and an actionness model. The classification model is trained to make predictions of which segments of a video depict an action and to classify the actions in the segments. The actionness model predicts whether any action is occurring in each segment, rather than predicting a particular type of action. This reduces the likelihood that the video localization system over-relies on contextual information in localizing actions in video. Furthermore, the classification model and the actionness model are trained based on weakly-labeled data, thereby reducing the cost and time required to generate training data for the video localization system.