# IMP: Instance Mask Projection for High Accuracy Semantic Segmentation of Things

arXiv: https://arxiv.org/abs/1906.06597 · HTML: https://mlsanity.com/paper/1906.06597/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2019.

## Built on by (most cited first, 5 of 5)

- [DiffAvatar: Simulation-Ready Garment Optimization with Differentiable Simulation](https://mlsanity.com/paper/2311.12194/) (2023)
- [Exemplar-Based Open-Set Panoptic Segmentation Network](https://mlsanity.com/paper/2105.08336/) (2021)
- [Semi-supervised Synthesis of High-Resolution Editable Textures for 3D Humans](https://mlsanity.com/paper/2103.17266/) (2021)
- [From Culture to Clothing: Discovering the World Events Behind A Century of Fashion Images](https://mlsanity.com/paper/2102.01690/) (2021)
- [Uncertainty Aware Proposal Segmentation for Unknown Object Detection](https://mlsanity.com/paper/2111.12866/) (2021)

## Abstract (arXiv)

In this work, we present a new operator, called Instance Mask Projection (IMP), which projects a predicted Instance Segmentation as a new feature for semantic segmentation. It also supports back propagation so is trainable end-to-end. Our experiments show the effectiveness of IMP on both Clothing Parsing (with complex layering, large deformations, and non-convex objects), and on Street Scene Segmentation (with many overlapping instances and small objects). On the Varied Clothing Parsing dataset (VCP), we show instance mask projection can improve 3 points on mIOU from a state-of-the-art Panoptic FPN segmentation approach. On the ModaNet clothing parsing dataset, we show a dramatic improvement of 20.4% absolutely compared to existing baseline semantic segmentation results. In addition, the instance mask projection operator works well on other (non-clothing) datasets, providing an improvement of 3 points in mIOU on Thing classes of Cityscapes, a self-driving dataset, on top of a state-of-the-art approach.