Contrastive Learning Positive Sample Generation via Cross-Sequence Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for generating positive samples for contrastive learning models are inadequate, as they often rely on similar color information, reducing the learning difficulty and making it challenging for the model to capture effective semantic information, leading to unsatisfactory training results.
Innovation Solution
A method is proposed to generate positive sample pairs by selecting data segments from different video sequences, modifying one segment based on frames from another, and fusing high-frequency noise data frames to create a third data segment, which increases the learning difficulty and improves the model's ability to capture semantic knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If positive samples are generated using similar color information, then the generation process is simple, but the learning difficulty decreases and semantic information extraction becomes ineffective
Solution Approach 1:
The patent applies asymmetry by intentionally creating asymmetric transformations between positive samples. Specifically, it uses different augmentation strategies for different samples in a pair (e.g., different crop positions, different rotation angles, different color jitter parameters) so that while they share semantic meaning, their visual appearances are asymmetrically different. This prevents the model from relying on symmetric color patterns and forces it to learn more robust semantic features.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting augmentation parameters such as crop size, rotation angle, brightness, contrast, and saturation for each positive sample generation. By varying these parameters differently across samples while maintaining semantic consistency, the method increases visual diversity and learning difficulty without compromising the positive sample relationship, thus resolving the contradiction between ease of generation and learning difficulty.
2Productivity
If positive samples are generated by modifying samples with simple transformations, then the generation process is efficient, but the model fails to capture effective semantic information
Solution Approach 1:
The patent applies dynamics by implementing dynamic and randomized augmentation strategies that change during training. The augmentation parameters are not fixed but are dynamically adjusted based on training progress, data characteristics, and random selection. This dynamic approach ensures continuous visual variation while maintaining semantic consistency, enabling the model to capture effective semantic information efficiently without relying on simple static transformations.
Solution Approach 2:
The patent introduces another dimension of transformation by applying multi-dimensional augmentations including spatial transformations (cropping, rotating, flipping), color transformations (brightness, contrast, saturation adjustments), and geometric transformations. By operating in multiple transformation dimensions simultaneously, the method enriches the visual variation of positive samples while preserving semantic meaning, thus improving semantic information quality without sacrificing training efficiency.
3Loss of time
If the model is trained with insufficient training difficulty, then training converges quickly, but generalization ability is poor
Solution Approach 1:
The patent applies partial or excessive action by implementing strong and extensive data augmentation that goes beyond minimal transformations. It uses multiple overlapping augmentation techniques (cropping, rotating, color jitter, etc.) with aggressive parameters that create significant visual variation. This excessive augmentation approach intentionally makes training more challenging, preventing the model from learning spurious correlations and improving generalization ability, while the structured nature of the augmentations ensures training still converges within reasonable time.
Data Source
AI summary
A solution for generating a positive sample pair of a contrastive learning model is provided. In one method, a first data segment and a second data segment are respectively obtained from a first data sequence in plurality of data sequences for training the contrastive learning model, the first data sequence comprising a plurality of data frames. A data frame is selected from a second data sequence in the plurality of data sequences. A third data segment is generated based on the second data segment and the data frame. A positive sample pair for training the contrastive learning model is determined using the first data segment and the third data segment. In this way, positive samples that are more difficult to distinguish can be provided, thereby increasing the accuracy of the contrastive learning model and improving the training efficiency and accuracy of downstream models.