  Modern object detection and instance segmentation networks stumble when
picking out humans in crowded or highly occluded scenes. Yet, these are often
scenarios where we require our detectors to work well. Many works have
approached this problem with model-centric improvements. While they have been
shown to work to some extent, these supervised methods still need sufficient
relevant examples (i.e. occluded humans) during training for the improvements
to be maximised. In our work, we propose a simple yet effective data-centric
approach, Occlusion Copy & Paste, to introduce occluded examples to models
during training - we tailor the general copy & paste augmentation approach to
tackle the difficult problem of same-class occlusion. It improves instance
segmentation performance on occluded scenarios for "free" just by leveraging on
existing large-scale datasets, without additional data or manual labelling
needed. In a principled study, we show whether various proposed add-ons to the
copy & paste augmentation indeed contribute to better performance. Our
Occlusion Copy & Paste augmentation is easily interoperable with any models: by
simply applying it to a recent generic instance segmentation model without
explicit model architectural design to tackle occlusion, we achieve
state-of-the-art instance segmentation performance on the very challenging
OCHuman dataset. Source code is available at
https://github.com/levan92/occlusion-copy-paste.
