Temporal Augmentation for Video Reasoning Model Generalization
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Solution Overview
Problem
Deep learning models for video reasoning struggle with limited data, leading to poor pattern recognition and increased risk of overfitting, which affects their ability to generalize well in practice.
Innovation Solution
The method involves randomly subsampling and reversing video frame sequences to create temporally augmented inputs for training a video reasoning model, allowing the model to focus on temporal order rather than spatial features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data augmentation techniques are applied to increase training data diversity, then model generalization improves, but training data processing complexity increases
Solution Approach 1:
The patent applies temporal inversion by reversing video frame sequences during training. This inversion technique creates augmented training data by playing video clips backward, forcing the model to learn temporal orderings and reducing overfitting to spatial features alone, thereby improving generalization without requiring additional video data collection
Solution Approach 2:
The patent modifies temporal parameters of video data by changing frame sequence order through random reversal. This parameter transformation creates diverse training examples from existing data, increasing effective training data diversity while maintaining computational efficiency through simple sequence manipulation rather than complex processing
2Measurement precision
If more training data is collected to improve model performance, then pattern recognition improves, but data collection cost and time increase
Solution Approach 1:
The patent creates copies of existing video training data by generating temporally inverted versions. Instead of collecting new video data, the system replicates and transforms existing clips through reversal, creating augmented training sets that improve pattern recognition capabilities without requiring additional data collection efforts or time investment
3Reliability
If complex data augmentation techniques are used to increase data diversity, then model robustness improves, but inference time increases
Solution Approach 1:
The patent performs temporal augmentation during the training phase rather than at inference time. By pre-processing training data with frame sequence reversal and teaching the model to recognize patterns regardless of temporal direction, the system builds robustness in advance. This preliminary action ensures that inference operations remain fast since no additional processing is needed during deployment
Data Source
AI summary
A method for augmenting video sequences in a video reasoning system is presented. The method includes randomly subsampling a sequence of video frames captured from one or more video cameras, randomly reversing the subsampled sequence of video frames to define a plurality of sub-sequences of randomly reversed video frames, training, in a training mode, a video reasoning model with temporally augmented input, including the plurality of sub-sequences of randomly reversed video frames, to make predictions over temporally augmented target classes, updating parameters of the video reasoning model by a machine leaning algorithm, and deploying, in an inference mode, the video reasoning model in the video reasoning system to make a final prediction related to a human action in the sequence of video frames.