Implicit Feature Refinement for Instance Segmentation

@article{Ma2021ImplicitFR,
  title={Implicit Feature Refinement for Instance Segmentation},
  author={Lufan Ma and Tiancai Wang and Bin Dong and Jiangpeng Yan and Xiu Li and Xiangyu Zhang},
  journal={Proceedings of the 29th ACM International Conference on Multimedia},
  year={2021}
}
We propose a novel implicit feature refinement module for high-quality instance segmentation. Existing image/video instance segmentation methods rely on explicitly stacked convolutions to refine instance features before the final prediction. In this paper, we first give an empirical comparison of different refinement strategies, which reveals that the widely-used four consecutive convolutions are not necessary. As an alternative, weight-sharing convolution blocks provides competitive… 
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UniInst: Unique Representation for End-to-End Instance Segmentation

  • Yimin OuRui Yang Xiu Li
  • Computer Science
    Neurocomputing
  • 2022
This work designs an instance-aware one-to-one assignment scheme, namely OYOR, which dynamically assigns one unique representation to each instance according to the matching quality between predictions and ground truths, and achieves competitive performance against mainstream methods on COCO test-dev.

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