Weakly-Supervised Online Action Segmentation Using Ordered Labels
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Solution Overview
Problem
The manual and time-consuming process of creating fully annotated video datasets for human action understanding is costly and prone to ambiguities in labeling start and end times of actions, limiting the scale and practicality of fully supervised video datasets.
Innovation Solution
A computer-implemented method for weakly-supervised online action segmentation that uses an ordered sequence of action labels to identify and label atomic actions in real-time through a neural network trained with multi-view video data, eliminating the need for subjective temporal annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used to create fully annotated video datasets, then action labeling accuracy can be achieved, but the process becomes time-consuming and expensive
Solution Approach 1:
The system enables self-service annotation by training the neural network to automatically generate action segmentation labels from video data. The model learns temporal boundaries and action types through self-supervised learning from raw video sequences, eliminating the need for manual frame-by-frame annotation while maintaining reasonable accuracy through iterative refinement on generated pseudo-labels
Solution Approach 2:
The system performs preliminary action by pre-training the neural network on large-scale video data to learn action patterns and temporal structures before deployment. This pre-training phase establishes the model's capability to perform action segmentation, which can then be applied automatically to new video datasets without requiring manual annotation for each dataset
2Loss of information
If manual annotation is used to create fully annotated video datasets, then comprehensive action labels can be obtained, but the cost increases significantly
Solution Approach 1:
The system enables self-service annotation by training the neural network to automatically generate action segmentation labels from video data. The model learns temporal boundaries and action types through self-supervised learning from raw video sequences, eliminating the need for manual frame-by-frame annotation while maintaining reasonable accuracy through iterative refinement on generated pseudo-labels
Solution Approach 2:
The system creates copies of action labels by generating pseudo-labels from the trained model that replicate the quality and structure of manual annotations. These synthesized labels can be used to create multiple annotated datasets from a single source, effectively multiplying the value of any initial annotated data without incurring proportional annotation costs
3Manufacturing precision
If subjective labeling of start and end times is used, then action temporal boundaries can be marked, but ambiguities in evaluation arise
Solution Approach 1:
The system implements feedback mechanisms where the neural network's predictions are continuously refined through comparison with ground truth data during training. The model receives feedback on its temporal boundary predictions and adjusts its parameters to minimize errors, leading to more consistent and reliable action segmentation that reduces evaluation ambiguities
Solution Approach 2:
The system changes parameters by learning optimal temporal boundary detection parameters from data rather than using fixed or subjective thresholds. The neural network adapts its internal parameters to capture the true temporal structure of actions, producing objective and consistent boundary markings that improve evaluation reliability across different datasets and annotators
Data Source
AI summary
A system and method for providing weakly-supervised online action segmentation that include receiving image data associated with multi-view videos of a procedure, wherein the procedure involves a plurality of atomic actions. The system and method also include analyzing the image data using weakly-supervised action segmentation to identify each of the plurality of atomic actions by using an ordered sequence of action labels. The system and method additionally include training a neural network with data pertaining to the plurality of atomic actions based on the weakly-supervised action segmentation. The system and method further include executing online action segmentation to label atomic actions that are occurring in real-time based on the plurality of atomic actions trained to the neural network.