On the Importance of Visual Context for Data Augmentation in Scene Understanding

Area of Science:

• Computer Vision
• Machine Learning
• Deep Learning

Background:

• Data augmentation is crucial for training deep neural networks (DNNs) in visual recognition.
• Simple image transformations improve performance, but task-specific knowledge yields greater gains.
• Object detection and segmentation tasks benefit significantly from effective data augmentation strategies.

Purpose of the Study:

• To enhance data augmentation for object detection, semantic segmentation, and instance segmentation.
• To address the challenge of context-aware object placement in augmented images.
• To improve model generalization and reduce overfitting in visual recognition systems.

Main Methods:

• Augmenting training images by blending objects using instance segmentation annotations.
• Developing an explicit context model using a convolutional neural network (CNN) to predict suitable object placement regions.
• Employing weakly-supervised learning for instance mask approximation when only bounding boxes are available.

Main Results:

• The proposed context-aware blending approach significantly improves object detection, semantic segmentation, and instance segmentation.
• Substantial performance gains were observed in limited annotation scenarios, particularly with only one category annotated.
• The method demonstrated effectiveness even with datasets lacking pixel-wise instance annotations, utilizing bounding boxes via weakly-supervised learning.

Conclusions:

• Context-aware data augmentation by blending objects is a powerful technique for improving DNNs in visual recognition tasks.
• The explicit context model effectively guides object placement, overcoming limitations of random pasting.
• This approach offers flexibility, working with both instance segmentation and bounding box annotations, and shows promise for low-annotation settings.