  Occlusion-aware instance-sensitive segmentation is a complex task generally
split into region-based segmentations, by approximating instances as their
bounding box. We address the showcase scenario of dense homogeneous layouts in
which this approximation does not hold. In this scenario, outlining unoccluded
instances by decoding a deep encoder becomes difficult, due to the translation
invariance of convolutional layers and the lack of complexity in the decoder.
We therefore propose a multicameral design composed of subtask-specific
lightweight decoder and encoder-decoder units, coupled in cascade to encourage
subtask-specific feature reuse and enforce a learning path within the decoding
process. Furthermore, the state-of-the-art datasets for occlusion-aware
instance segmentation contain real images with few instances and occlusions
mostly due to objects occluding the background, unlike dense object layouts. We
thus also introduce a synthetic dataset of dense homogeneous object layouts,
namely Mikado, which extensibly contains more instances and inter-instance
occlusions per image than these public datasets. Our extensive experiments on
Mikado and public datasets show that ordinal multiscale units within the
decoding process prove more effective than state-of-the-art design patterns for
capturing position-sensitive representations. We also show that Mikado is
plausible with respect to real-world problems, in the sense that it enables the
learning of performance-enhancing representations transferable to real images,
while drastically reducing the need of hand-made annotations for finetuning.
The proposed dataset will be made publicly available.
