# Bounding Box Embedding for Single Shot Person Instance Segmentation

Source: [Bounding Box Embedding for Single Shot Person Instance Segmentation](<https://www.sota2.com/research/paper/68fa64991e209131cea0561f>)

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

Data updated: 2025-10-25T21:07:03.168Z

Authors: Jacob Richeimer, Jonathan Mitchell

Published: 2018-07-20T00:30:30.000Z

## Abstract

We present a bottom-up approach for the task of object instance segmentation using a single-shot model. The proposed model employs a fully convolutional network which is trained to predict class-wise segmentation masks as well as the bounding boxes of the object instances to which each pixel belongs. This allows us to group object pixels into individual instances. Our network architecture is based on the DeepLabv3+ model, and requires only minimal extra computation to achieve pixel-wise instance assignments. We apply our method to the task of person instance segmentation, a common task relevant to many applications. We train our model with COCO data and report competitive results for the person class in the COCO instance segmentation task.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1807.07674>)

[PDF](<https://arxiv.org/pdf/1807.07674.pdf>)