# Ensembling Instance and Semantic Segmentation for Panoptic Segmentation

Source: [Ensembling Instance and Semantic Segmentation for Panoptic Segmentation](<https://www.sota2.com/research/paper/68fa6f211e209131cea43e4f>)

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

Data updated: 2025-10-26T01:13:37.852Z

Authors: Mehmet Yildirim, Yogesh Langhe

Published: 2023-04-20T14:02:01.000Z

## Abstract

We demonstrate our solution for the 2019 COCO panoptic segmentation task. Our method first performs instance segmentation and semantic segmentation separately, then combines the two to generate panoptic segmentation results. To enhance the performance, we add several expert models of Mask R-CNN in instance segmentation to tackle the data imbalance problem in the training data; also HTC model is adopted yielding our best instance segmentation results. In semantic segmentation, we trained several models with various backbones and use an ensemble strategy which further boosts the segmentation results. In the end, we analyze various combinations of instance and semantic segmentation, and report on their performance for the final panoptic segmentation results. Our best model achieves $PQ$ 47.1 on 2019 COCO panoptic test-dev data.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2304.10326>)

[PDF](<https://arxiv.org/pdf/2304.10326.pdf>)