# SiamPolar: Semi-supervised Realtime Video Object Segmentation with Polar Representation

arXiv: https://arxiv.org/abs/2110.14773 · HTML: https://mlsanity.com/paper/2110.14773/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 46 references that are in the map.
- First posted on arXiv 27 Oct 2021.

## Builds on (most cited first, 27 of 27)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [The 2017 DAVIS Challenge on Video Object Segmentation](https://mlsanity.com/paper/1704.00675/) (2017)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks](https://mlsanity.com/paper/1812.11703/) (2018)
- [Fast Online Object Tracking and Segmentation: A Unifying Approach](https://mlsanity.com/paper/1812.05050/) (2018)
- [One-Shot Video Object Segmentation](https://mlsanity.com/paper/1611.05198/) (2016)
- [Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network](https://mlsanity.com/paper/1703.02719/) (2017)
- [PolarMask: Single Shot Instance Segmentation with Polar Representation](https://mlsanity.com/paper/1909.13226/) (2019)
- [FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation](https://mlsanity.com/paper/1902.09513/) (2019)
- [Deeper and Wider Siamese Networks for Real-Time Visual Tracking](https://mlsanity.com/paper/1901.01660/) (2019)
- [Learning Video Object Segmentation from Static Images](https://mlsanity.com/paper/1612.02646/) (2016)
- [Efficient Video Object Segmentation via Network Modulation](https://mlsanity.com/paper/1802.01218/) (2018)
- [Video Object Segmentation Without Temporal Information](https://mlsanity.com/paper/1709.06031/) (2017)
- [SegFlow: Joint Learning for Video Object Segmentation and Optical Flow](https://mlsanity.com/paper/1709.06750/) (2017)
- [Revisiting the Sibling Head in Object Detector](https://mlsanity.com/paper/2003.07540/) (2020)
- [Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning](https://mlsanity.com/paper/1804.03131/) (2018)
- [Fast and Accurate Online Video Object Segmentation via Tracking Parts](https://mlsanity.com/paper/1806.02323/) (2018)
- [Video Object Segmentation with Joint Re-identification and Attention-Aware Mask Propagation](https://mlsanity.com/paper/1803.04242/) (2018)
- [Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations](https://mlsanity.com/paper/1809.04766/) (2018)
- [CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRF](https://mlsanity.com/paper/1803.09453/) (2018)
- [MaskRNN: Instance Level Video Object Segmentation](https://mlsanity.com/paper/1803.11187/) (2018)
- [Video Propagation Networks](https://mlsanity.com/paper/1612.05478/) (2016)
- [Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks](https://mlsanity.com/paper/1708.05137/) (2017)
- [Video Object Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) Setting](https://mlsanity.com/paper/1810.07733/) (2018)

## Built on by (most cited first, 1 of 1)

- [TIVE: A Toolbox for Identifying Video Instance Segmentation Errors](https://mlsanity.com/paper/2210.08856/) (2022)

## Abstract (arXiv)

Video object segmentation (VOS) is an essential part of autonomous vehicle navigation. The real-time speed is very important for the autonomous vehicle algorithms along with the accuracy metric. In this paper, we propose a semi-supervised real-time method based on the Siamese network using a new polar representation. The input of bounding boxes is initialized rather than the object masks, which are applied to the video object detection tasks. The polar representation could reduce the parameters for encoding masks with subtle accuracy loss so that the algorithm speed can be improved significantly. An asymmetric siamese network is also developed to extract the features from different spatial scales. Moreover, the peeling convolution is proposed to reduce the antagonism among the branches of the polar head. The repeated cross-correlation and semi-FPN are designed based on this idea. The experimental results on the DAVIS-2016 dataset and other public datasets demonstrate the effectiveness of the proposed method.