  Detecting occluded objects still remains a challenge for state-of-the-art
object detectors. The objective of this work is to improve the detection for
such objects, and thereby improve the overall performance of a modern object
detector.
  To this end we make the following four contributions: (1) We propose a simple
'plugin' module for the detection head of two-stage object detectors to improve
the recall of partially occluded objects. The module predicts a tri-layer of
segmentation masks for the target object, the occluder and the occludee, and by
doing so is able to better predict the mask of the target object. (2) We
propose a scalable pipeline for generating training data for the module by
using amodal completion of existing object detection and instance segmentation
training datasets to establish occlusion relationships. (3) We also establish a
COCO evaluation dataset to measure the recall performance of partially occluded
and separated objects. (4) We show that the plugin module inserted into a
two-stage detector can boost the performance significantly, by only fine-tuning
the detection head, and with additional improvements if the entire architecture
is fine-tuned. COCO results are reported for Mask R-CNN with Swin-T or Swin-S
backbones, and Cascade Mask R-CNN with a Swin-B backbone.
