Data Augmentation for Object Detection via Progressive and Selective Instance-Switching

Abstract

Collection of massive well-annotated samples is effective in improving object detection performance but is extremely laborious and costly. Instead of data collection and annotation, the recently proposed Cut-Paste methods [12, 15] show the potential to augment training dataset by cutting foreground objects and pasting them on proper new backgrounds. However, existing Cut-Paste methods cannot guarantee synthetic images always precisely model visual context, and all of them require external datasets. To handle above issues, this paper proposes a simple yet effective instance-switching (IS) strategy, which generates new training data by switching instances of same class from different images. Our IS naturally preserves contextual coherence in the original images while requiring no external dataset. For guiding our IS to obtain better object performance, we explore issues of instance imbalance and class importance in datasets, which frequently occur and bring adverse effect on detection performance. To this end, we propose a novel Progressive and Selective Instance-Switching (PSIS) method to augment training data for object detection. The proposed PSIS enhances instance balance by combining selective re-sampling with a class-balanced loss, and considers class importance by progressively augmenting training dataset guided by detection performance. The experiments are conducted on the challenging MS COCO benchmark, and results demonstrate our PSIS brings clear improvement over various state-of-the-art detectors (e.g., Faster R-CNN, FPN, Mask R-CNN and SNIPER), showing the superiority and generality of our PSIS. Code and models are available at: https://github.com/Hwang64/PSIS.

References in corpus (15)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• SMOTE: Synthetic Minority Over-sampling Technique
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• A systematic study of the class imbalance problem in convolutional neural networks
• Improved Regularization of Convolutional Neural Networks with Cutout
• DSSD : Deconvolutional Single Shot Detector
• Pixel Recurrent Neural Networks
• Random Erasing Data Augmentation
• Data Augmentation using Random Image Cropping and Patching for Deep CNNs
• Data Augmentation by Pairing Samples for Images Classification
• Class-Balanced Loss Based on Effective Number of Samples
• Context-Aware Synthesis and Placement of Object Instances
• Dynamic Curriculum Learning for Imbalanced Data Classification
• Learning a Discriminative Model for the Perception of Realism in Composite Images
• On the Importance of Visual Context for Data Augmentation in Scene Understanding

Cited by in corpus (7)

• Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
• Making Images Real Again: A Comprehensive Survey on Deep Image Composition
• Distribution Alignment: A Unified Framework for Long-tail Visual Recognition
• WQT and DG-YOLO: towards domain generalization in underwater object detection
• OPA: Object Placement Assessment Dataset
• Scale-aware Automatic Augmentation for Object Detection
• Object Detection for Understanding Assembly Instruction Using Context-aware Data Augmentation and Cascade Mask R-CNN