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Collection of massive well-annotated samples is effective in improving objectdetection performance but is extremely laborious and costly. Instead of datacollection and annotation, the recently proposed Cut-Paste methods [12, 15]show the potential to augment training dataset by cutting foreground objectsand pasting them on proper new backgrounds. However, existing Cut-Paste methodscannot guarantee synthetic images always precisely model visual context, andall of them require external datasets. To handle above issues, this paperproposes a simple yet effective instance-switching (IS) strategy, whichgenerates new training data by switching instances of same class from differentimages. Our IS naturally preserves contextual coherence in the original imageswhile requiring no external dataset. For guiding our IS to obtain better objectperformance, we explore issues of instance imbalance and class importance indatasets, which frequently occur and bring adverse effect on detectionperformance. To this end, we propose a novel Progressive and SelectiveInstance-Switching (PSIS) method to augment training data for object detection.The proposed PSIS enhances instance balance by combining selective re-samplingwith a class-balanced loss, and considers class importance by progressivelyaugmenting training dataset guided by detection performance. The experimentsare conducted on the challenging MS COCO benchmark, and results demonstrate ourPSIS brings clear improvement over various state-of-the-art detectors (e.g.,Faster R-CNN, FPN, Mask R-CNN and SNIPER), showing the superiority andgenerality of our PSIS. Code and models are available at:https://github.com/Hwang64/PSIS.

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