AutoChainer: Automatic Data Augmentation for Stroke-based Input

Paper Title

AutoChainer: Automatic Data Augmentation for Stroke-based Input

Publication Info

• Topic area: Automatic data augmentation for stroke-based input in gesture recognition and related tasks.
• Keywords: Data augmentation, stroke-based input, gesture recognition, AutoDA, AutoChainer, deep learning, human-computer interaction, classification accuracy, visual quality, customization.

Background and Problem

• Problem / challenge: Stroke-based gesture recognition systems often face challenges due to limited and highly variable training data. Existing data augmentation (DA) methods, while effective in other domains like computer vision, are underexplored for stroke-based data. Current methods like AVC lack flexibility and produce visually distorted samples.
• Significance: Reliable stroke-based gesture recognition systems are critical for human-computer interaction (HCI) applications, especially in scenarios with limited user-provided data, such as for individuals with motor impairments.
• Motivation and related work: Prior work in gesture recognition has shifted from template matching to deep learning (DL) models, which require large labeled datasets. Automatic data augmentation (AutoDA) methods have shown success in image classification but have not been tested on stroke-based data. Existing stroke-based DA methods like AVC are rigid and optimized for specific datasets, limiting their generalizability.

Solution

• Proposed approach: AutoChainer, a search-free AutoDA method for stroke-based data, applies random chains of augmentation transformations to generate diverse and interpretable samples.
• Novelty:
  1. Introduction of AutoChainer, inspired by UniformAugment, tailored for stroke-based input.
  2. Evaluation of AutoChainer variants in terms of classification accuracy, execution speed, and visual quality.
  3. Demonstration of AutoChainer’s generalizability across eight diverse datasets and multiple classifiers.
  4. Customization options for task-specific requirements, such as balancing accuracy and speed.
• Procedure and key techniques:
  • Randomly chain augmentation transformations from a predefined pool.
  • Test different chain lengths (e.g., AC1, AC1,6) and transformation ranges (Original and Extended).
  • Evaluate performance on eight datasets using various models, including RNNs, Transformers, and $Q recognizers.
  • Analyze visual quality using Fréchet Inception Distance (FID) and gesture heatmaps.

Results

• Concrete findings:
  • AutoChainer variants significantly improved classification accuracy across all datasets, with AC5 and AC1,6 often outperforming AVC.
  • Extended transformation ranges yielded better accuracy than Original ranges.
  • AutoChainer produced samples with lower FID scores than AVC, indicating better visual fidelity.
• Advantage over baselines:
  • Outperformed AVC in 4 out of 8 datasets and achieved comparable results in others.
  • Demonstrated superior generalization across diverse datasets and models.
  • Faster execution times with optimized presets (e.g., Turbo preset reduced execution time significantly).
• Experiments / evaluation:
  • Conducted classification tasks on eight datasets, including unistroke and multistroke gestures, handwritten characters, and signatures.
  • Compared AutoChainer against AVC, GPSR, and optimized models.
  • Evaluated visual quality using FID and gesture heatmaps.
• Limitations and future work:
  • Limited to augmentation operations derived from prior work; future research could explore additional transformations.
  • Focused on search-free AutoDA methods; search-based methods may offer further improvements.
  • Did not combine model optimization with data augmentation; future work could investigate this synergy.
  • Potential to explore applications in domains like medical data augmentation and large foundation models for gestures.

Summary

AutoChainer is a search-free AutoDA method designed for stroke-based input, addressing challenges in gesture recognition with limited training data. It improves classification accuracy, enhances visual quality, and offers customization for task-specific needs. Evaluated on eight datasets and multiple models, AutoChainer consistently outperformed or matched state-of-the-art methods like AVC. Its flexibility and efficiency make it a valuable tool for building reliable stroke-based recognition systems, with applications in HCI and beyond. Future work could explore additional augmentation techniques, model optimization, and broader applications.