Action Segmentation Model Causal Explanation via Gradient Propagation
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Solution Overview
Problem
Current video action segmentation models lack causal explainability, making it difficult to interpret and understand the temporal structure of actions in complex, long-duration videos, especially in domains like manufacturing where fine-grain action inference is challenging.
Innovation Solution
The integration of a causation determiner circuitry that propagates gradient information from a pre-trained action segmentation model to identify important antecedent frames, providing causal explanations by calculating pooled importance scores and applying rules to enhance interpretability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a pre-trained action segmentation model is used to predict actions in video frames, then action prediction capability is improved, but causal explainability deteriorates
Solution Approach 1:
The patent introduces gradient information as an intermediary element that mediates between the action segmentation model predictions and the input video frames. By computing gradients of the loss function with respect to input features and propagating them backward, the system identifies which frames causally influence predictions without modifying the original model architecture. This intermediary gradient mechanism enables causal explanation while preserving prediction accuracy.
Solution Approach 2:
The patent replaces traditional mechanical interpretation methods with gradient-based information propagation. Instead of relying on model architecture analysis or feature visualization techniques, the system uses automatic differentiation to compute exact causal relationships through gradient flow, substituting mechanical interpretation approaches with a mathematical field-based method.
2Loss of information
If gradient information is propagated to identify important antecedent frames, then causal explainability is improved, but computational complexity deteriorates
Solution Approach 1:
The patent performs gradient computation during the model training phase rather than during inference. By pre-computing and storing gradient information about frame importance while the model is being trained, the system prepares causal explanation data in advance. This preliminary action reduces the computational burden during actual video analysis, as the gradient propagation infrastructure is already established.
Solution Approach 2:
The action segmentation model serves its own explanatory needs by generating gradient information about its own predictions. The model's automatic differentiation mechanism inherently provides the causal relationships needed for explanation without requiring separate analysis systems. This self-service approach eliminates the need for external interpretation tools and reduces overall system complexity.
3Ease of operation
If pooled importance scores are calculated from gradient magnitudes, then interpretability is improved, but processing time deteriorates
Solution Approach 1:
The patent extracts only the essential gradient magnitude information needed for interpretation, discarding redundant directional and magnitude sign data. By taking out only the absolute importance scores rather than full gradient vectors, the system reduces the amount of data that needs to be processed and stored, thereby decreasing processing time while maintaining interpretability.
Solution Approach 2:
The patent applies different processing levels to different parts of the gradient information. Rather than uniformly processing all gradient data at high resolution, the system focuses computational effort on identifying and processing only the most important antecedent frames based on gradient magnitude thresholds. This local quality approach processes critical information in detail while summarizing or skipping less important regions.
Data Source
AI summary
Systems, apparatus, articles of manufacture, and methods are disclosed to enhance action segmentation model with causal explanation capability. An example apparatus includes an interface circuitry to access a pre-trained action segmentation model, instructions, and processor circuitry to at least one of instantiate or execute the machine readable instructions to obtain action segmentation data from the pre-trained action segmentation model, the action segmentation data indicating action prediction for one or more frames of a video sequence, combine the obtained action segmentation data with input features extracted from the one or more video frames, and identify an antecedent action of at least one frame of the video sequence based on pooled importance scores for the frame, the pooled importance scores being calculated from the combined action segmentation data and input features.