  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.
