Generative image models are increasingly being used for training data
augmentation in vision tasks. In the context of automotive object detection,
methods usually focus on producing augmented frames that look as realistic as
possible, for example by replacing real objects with generated ones. Others try
to maximize the diversity of augmented frames, for example by pasting lots of
generated objects onto existing backgrounds. Both perspectives pay little
attention to the locations of objects in the scene. Frame layouts are either
reused with little or no modification, or they are random and disregard realism
entirely. In questo lavoro, we argue that optimal data augmentation should also
include realistic augmentation of layouts. We introduce a scene-aware
probabilistic location model that predicts where new objects can realistically
be placed in an existing scene. By then inpainting objects in these locations
with a generative model, we obtain much stronger augmentation performance than
existing approaches. We set a new state of the art for generative data
augmentation on two automotive object detection tasks, achieving up to
$2.8\times$ higher gains than the best competing approach ($+1.4$ vs. $+0.5$
mAP boost). We also demonstrate significant improvements for instance
segmentation.

Questo articolo esplora i giri e le loro implicazioni.

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