Bounding Box Embedding for Single Shot Person Instance Segmentation

TL;DR

This paper introduces a single-shot, bottom-up approach for person instance segmentation that predicts class masks and bounding boxes simultaneously, enabling efficient grouping of pixels into individual instances.

Contribution

It proposes a novel method combining DeepLabv3+ with bounding box prediction for efficient, accurate person instance segmentation with minimal additional computation.

Findings

Achieves competitive results on COCO person instance segmentation

Uses minimal extra computation compared to existing methods

Effectively groups pixels into individual instances

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.