Free Performance Gain from Mixing Multiple Partially Labeled Samples in Multi-label Image Classification
Chak Fong Chong, Jielong Guo, Xu Yang, Wei Ke, Yapeng Wang·May 24, 2024
Summary
This paper introduces LogicMix, a novel data augmentation method for multi-label image classification with partially labeled datasets. It improves upon Mixup by mixing labels using logical OR, allowing for correct interpolation of unknown labels and handling multiple samples to create visually confusing augmented data. LogicMix is more general and effective, with minimal computational overhead, and can be easily integrated into existing frameworks. By combining LogicMix with other techniques like RandAugment, Curriculum Labeling, and Category-wise Fine-Tuning, state-of-the-art performance is achieved on MS-COCO, VG-200, and Pascal VOC 2007 benchmarks. The method outperforms existing Mixup-based methods and shows promise in addressing the challenges of multi-label classification with incomplete data. Experiments demonstrate its effectiveness and versatility, making it a valuable addition to the field.
Introduction
Background
Overview of multi-label image classification and partially labeled datasets
Challenges with Mixup in multi-label scenarios
Objective
To develop a novel data augmentation method for improved performance with partially labeled data
Address the limitations of Mixup in multi-label settings
Method
LogicMix Algorithm
Label Mixing using Logical OR
Definition and implementation of logical OR for label interpolation
Handling unknown and partially labeled samples
Computational Efficiency
Comparison with Mixup in terms of computational overhead
Integration with Existing Frameworks
Compatibility and ease of implementation in popular deep learning libraries
Complementary Techniques
1. Combining with RandAugment
Description of RandAugment and its integration with LogicMix
2. Curriculum Labeling
Integration of LogicMix with curriculum learning for progressive label exposure
3. Category-wise Fine-Tuning
Fine-tuning strategy using LogicMix for different label categories
Experiments and Evaluation
Datasets
MS-COCO, VG-200, and Pascal VOC 2007 benchmarks
Performance Comparison
State-of-the-art results achieved with LogicMix
Improvement over Mixup-based methods
Ablation Studies
Analysis of LogicMix's impact on different data scenarios
Visual Results
Examples of augmented data and their effect on model performance
Conclusion
Summary of LogicMix's effectiveness and versatility in multi-label image classification
Implications for future research and practical applications
Limitations and potential directions for improvement
Basic info
papers
computer vision and pattern recognition
artificial intelligence
Advanced features