Centroid Contrastive Learning for Long-Tailed Class Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large-scale natural datasets exhibit a long-tailed distribution with class imbalance, posing challenges for accurate recognition of tail classes, and existing methods like manual curation and data re-sampling are resource-intensive and unsatisfactory.
Innovation Solution
The interpolative centroid contrastive learning (ICCL) framework projects data samples into a low-dimensional embedding space, creates class centroids, and uses virtual training samples through class-agnostic and class-aware sampling to interpolate images, training a neural model with contrastive similarity matching to improve tail-class representation without distorting head-class representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual curation is used to balance class distribution, then tail class recognition accuracy is improved, but resource consumption and cost increase significantly
Solution Approach 1:
The system automatically balances class distribution through self-service mechanisms: virtual samples are generated via interpolation between real samples, and class-aware sampling automatically adjusts sample selection based on class frequency. This eliminates the need for manual curation while achieving balanced representation of tail classes.
Solution Approach 2:
Virtual training samples are created by copying and interpolating features from existing real samples. The system generates synthetic representations of tail class samples through feature interpolation, effectively multiplying the available training data without requiring additional physical resources or manual annotation.
2Measurement precision
If data re-sampling is applied to address class imbalance, then tail class representation is improved, but head class representation may be distorted
Solution Approach 1:
The system applies different sampling strategies to different classes: class-agnostic sampling handles head classes with uniform sampling, while class-aware sampling focuses on tail classes with oversampling. This local differentiation ensures each class receives appropriate attention without distorting the natural distribution of head classes.
Solution Approach 2:
The sampling process is segmented into two independent pathways: one for head classes (class-agnostic) and one for tail classes (class-aware). This segmentation allows the system to optimize for tail class representation while preserving head class characteristics through separate, non-interfering processing streams.
3Productivity
If standard training is performed on imbalanced datasets, then training efficiency is maintained, but tail class recognition accuracy deteriorates
Solution Approach 1:
The system performs preliminary actions by generating virtual training samples and computing class centroids before the actual training process. Interpolated samples are pre-computed and class centroids are pre-established, so that when training begins, the model already has balanced representation and clear class boundaries, improving tail class accuracy without extending training time.
Solution Approach 2:
Class centroids serve as intermediary representations that mediate between real samples and virtual interpolated samples. The contrastive learning process uses these centroids as reference points to guide the model in learning discriminative features for tail classes, bridging the gap between imbalanced real data and balanced virtual data.
Data Source
AI summary
An interpolative centroid contrastive learning (ICCL) framework is disclosed for learning a more discriminative representation for tail classes. Specifically, data samples, such as natural images, are projected into a low-dimensional embedding space, and class centroids for respective classes are created as average embeddings of samples that belong to a respective class. Virtual training samples are then created by interpolating two images from two samplers: a class-agnostic sampler which returns all images from both the head class and the tail class with an equal probability, and a class-aware sampler which focuses more on tail-class images by sampling images from the tail class with a higher probability compared to images from the head class. The sampled images, e.g., images from the class-agnostic sampler and images from the class-aware sampler may be interpolated to generate interpolated images.