ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning
Paper in proceeding, 2021

The state of the art in semantic segmentation is steadily increasing in performance, resulting in more precise and reliable segmentations in many different applications. However, progress is limited by the cost of generating labels for training, which sometimes requires hours of manual labor for a single image. Because of this, semi-supervised methods have been applied to this task, with varying degrees of success. A key challenge is that common augmentations used in semi-supervised classification are less effective for semantic segmentation. We propose a novel data augmentation mechanism called ClassMix, which generates augmentations by mixing unlabelled samples, by leveraging on the network's predictions for respecting object boundaries. We evaluate this augmentation technique on two common semi-supervised semantic segmentation benchmarks, showing that it attains state-of-the-art results. Lastly, we also provide extensive ablation studies comparing different design decisions and training regimes.

data augmentation

semantic segmentation

semi-supervised learning


Viktor Olsson

Volvo Cars

Student at Chalmers

Wilhelm Tranheden

Student at Chalmers

Volvo Cars

Lennart Svensson

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Juliano Pinto

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021

9780738142661 (ISBN)

IEEE/CVF Winter Conference on Applications of Computer Vision
Online, Japan,

Probabilistic models and deep learning - bridging the gap

Wallenberg AI, Autonomous Systems and Software Program, -- .

Subject Categories

Computer Science

Computer Vision and Robotics (Autonomous Systems)

Medical Image Processing



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