  Existing instance segmentation methods have achieved impressive performance
but still suffer from a common dilemma: redundant representations (e.g.,
multiple boxes, grids, and anchor points) are inferred for one instance, which
leads to multiple duplicated predictions. Thus, mainstream methods usually rely
on a hand-designed non-maximum suppression (NMS) post-processing step to select
the optimal prediction result, which hinders end-to-end training. To address
this issue, we propose a box-free and NMS-free end-to-end instance segmentation
framework, termed UniInst, that yields only one unique representation for each
instance. Specifically, we design an instance-aware one-to-one assignment
scheme, namely Only Yield One Representation (OYOR), which dynamically assigns
one unique representation to each instance according to the matching quality
between predictions and ground truths. Then, a novel prediction re-ranking
strategy is elegantly integrated into the framework to address the misalignment
between the classification score and the mask quality, enabling the learned
representation to be more discriminative. With these techniques, our UniInst,
the first FCN-based box-free and NMS-free instance segmentation framework,
achieves competitive performance, e.g., 39.0 mask AP using ResNet-50-FPN and
40.2 mask AP using ResNet-101-FPN, against mainstream methods on COCO test-dev
2017. Moreover, the proposed instance-aware method is robust to occlusion
scenes, outperforming common baselines by remarkable mask AP on the
heavily-occluded OCHuman benchmark. Code is available at
https://github.com/b03505036/UniInst.
