# Proposal-free Network for Instance-level Object Segmentation

arXiv: https://arxiv.org/abs/1509.02636 · HTML: https://mlsanity.com/paper/1509.02636/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2015.

## Built on by (most cited first, 15 of 15)

- [Spatial Feature Calibration and Temporal Fusion for Effective One-stage Video Instance Segmentation](https://mlsanity.com/paper/2104.05606/) (2021)
- [Semantic Image Segmentation: Two Decades of Research](https://mlsanity.com/paper/2302.06378/) (2023)
- [3D Instance Segmentation of MVS Buildings](https://mlsanity.com/paper/2112.09902/) (2021)
- [DeepSportLab: a Unified Framework for Ball Detection, Player Instance Segmentation and Pose Estimation in Team Sports Scenes](https://mlsanity.com/paper/2112.00627/) (2021)
- [VideoClick: Video Object Segmentation with a Single Click](https://mlsanity.com/paper/2101.06545/) (2021)
- [Beta R-CNN: Looking into Pedestrian Detection from Another Perspective](https://mlsanity.com/paper/2210.12758/) (2022)
- [Unsupervised domain adaptation for clinician pose estimation and instance segmentation in the operating room](https://mlsanity.com/paper/2108.11801/) (2021)
- [Real-time Instance Segmentation with Discriminative Orientation Maps](https://mlsanity.com/paper/2106.12204/) (2021)
- [FisheyeDetNet: 360deg Surround view Fisheye Camera based Object Detection System for Autonomous Driving](https://mlsanity.com/paper/2404.13443/) (2024)
- [Deep Instance Segmentation and Visual Servoing to Play Jenga with a Cost-Effective Robotic System](https://mlsanity.com/paper/2211.07977/) (2022)
- [Semantic Segmentation and Object Detection Towards Instance Segmentation: Breast Tumor Identification](https://mlsanity.com/paper/2108.03287/) (2021)
- [A wearable sensor vest for social humanoid robots with GPGPU, IoT, and modular software architecture](https://mlsanity.com/paper/2201.02192/) (2022)
- [Implementing a Real-Time, YOLOv5 based Social Distancing Measuring System for Covid-19](https://mlsanity.com/paper/2204.03350/) (2022)
- [Training a universal instance segmentation network for live cell images of various cell types and imaging modalities](https://mlsanity.com/paper/2207.14347/) (2022)
- [Multi-scale attention-based instance segmentation for measuring crystals with large size variation](https://mlsanity.com/paper/2401.03939/) (2024)

## Abstract (arXiv)

Instance-level object segmentation is an important yet under-explored task. The few existing studies are almost all based on region proposal methods to extract candidate segments and then utilize object classification to produce final results. Nonetheless, generating accurate region proposals itself is quite challenging. In this work, we propose a Proposal-Free Network (PFN ) to address the instance-level object segmentation problem, which outputs the instance numbers of different categories and the pixel-level information on 1) the coordinates of the instance bounding box each pixel belongs to, and 2) the confidences of different categories for each pixel, based on pixel-to-pixel deep convolutional neural network. All the outputs together, by using any off-the-shelf clustering method for simple post-processing, can naturally generate the ultimate instance-level object segmentation results. The whole PFN can be easily trained in an end-to-end way without the requirement of a proposal generation stage. Extensive evaluations on the challenging PASCAL VOC 2012 semantic segmentation benchmark demonstrate that the proposed PFN solution well beats the state-of-the-arts for instance-level object segmentation. In particular, the APr over 20 classes at 0.5 IoU reaches 58.7% by PFN, significantly higher than 43.8% and 46.3% by the state-of-the-art algorithms, SDS [9] and [16], respectively.