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

Source: [IMP: Instance Mask Projection for High Accuracy Semantic Segmentation of Things](<https://www.sota2.com/research/paper/68fa65df1e209131cea0cfd7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T05:58:47.572Z

Authors: Cheng-Yang Fu, Tamara L. Berg, Alexander C. Berg

Published: 2019-06-15T17:58:07.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1906.06597>)

[PDF](<https://arxiv.org/pdf/1906.06597.pdf>)