# Enhancing Generic Segmentation with Learned Region Representations

arXiv: https://arxiv.org/abs/1911.08564 · HTML: https://mlsanity.com/paper/1911.08564/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2019.

## Built on by (most cited first, 4 of 4)

- [GOSS: Towards Generalized Open-set Semantic Segmentation](https://mlsanity.com/paper/2203.12116/) (2022)
- [Mini-batch graphs for robust image classification](https://mlsanity.com/paper/2105.03237/) (2021)
- [Rethinking Interactive Image Segmentation: Feature Space Annotation](https://mlsanity.com/paper/2101.04378/) (2021)
- [Assessing hierarchies by their consistent segmentations](https://mlsanity.com/paper/2204.04969/) (2022)

## Abstract (arXiv)

Current successful approaches for generic (non-semantic) segmentation rely mostly on edge detection and have leveraged the strengths of deep learning mainly by improving the edge detection stage in the algorithmic pipeline. This is in contrast to semantic and instance segmentation, where DNNs are applied directly to generate pixel-wise segment representations. We propose a new method for learning a pixel-wise representation that reflects segment relatedness. This representation is combined with an edge map to yield a new segmentation algorithm. We show that the representations themselves achieve state-of-the-art segment similarity scores. Moreover, the proposed combined segmentation algorithm provides results that are either state of the art or improve upon it, for most quality measures.