# Fast Pixel-Matching for Video Object Segmentation

arXiv: https://arxiv.org/abs/2107.04279 · HTML: https://mlsanity.com/paper/2107.04279/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 57 references that are in the map.
- First posted on arXiv 9 Jul 2021.

## Builds on (most cited first, 30 of 37)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [The 2017 DAVIS Challenge on Video Object Segmentation](https://mlsanity.com/paper/1704.00675/) (2017)
- [Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation](https://mlsanity.com/paper/1901.02985/) (2019)
- [One-Shot Video Object Segmentation](https://mlsanity.com/paper/1611.05198/) (2016)
- [SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning](https://mlsanity.com/paper/1611.05594/) (2016)
- [FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation](https://mlsanity.com/paper/1902.09513/) (2019)
- [Learning Video Object Segmentation from Static Images](https://mlsanity.com/paper/1612.02646/) (2016)
- [Online Adaptation of Convolutional Neural Networks for Video Object Segmentation](https://mlsanity.com/paper/1706.09364/) (2017)
- [Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation](https://mlsanity.com/paper/1904.06807/) (2019)
- [Video Object Segmentation Without Temporal Information](https://mlsanity.com/paper/1709.06031/) (2017)
- [Self-Guided and Cross-Guided Learning for Few-Shot Segmentation](https://mlsanity.com/paper/2103.16129/) (2021)
- [VideoMatch: Matching based Video Object Segmentation](https://mlsanity.com/paper/1809.01123/) (2018)
- [Reliability Does Matter: An End-to-End Weakly Supervised Semantic Segmentation Approach](https://mlsanity.com/paper/1911.08039/) (2019)
- [PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation](https://mlsanity.com/paper/1807.09190/) (2018)
- [Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning](https://mlsanity.com/paper/1804.03131/) (2018)
- [A Generative Appearance Model for End-to-end Video Object Segmentation](https://mlsanity.com/paper/1811.11611/) (2018)
- [RANet: Ranking Attention Network for Fast Video Object Segmentation](https://mlsanity.com/paper/1908.06647/) (2019)
- [Video Object Segmentation with Joint Re-identification and Attention-Aware Mask Propagation](https://mlsanity.com/paper/1803.04242/) (2018)
- [A Transductive Approach for Video Object Segmentation](https://mlsanity.com/paper/2004.07193/) (2020)
- [Structure-Consistent Weakly Supervised Salient Object Detection with Local Saliency Coherence](https://mlsanity.com/paper/2012.04404/) (2020)
- [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)

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

- [Democracy Does Matter: Comprehensive Feature Mining for Co-Salient Object Detection](https://mlsanity.com/paper/2203.05787/) (2022)

## Abstract (arXiv)

Video object segmentation, aiming to segment the foreground objects given the annotation of the first frame, has been attracting increasing attentions. Many state-of-the-art approaches have achieved great performance by relying on online model updating or mask-propagation techniques. However, most online models require high computational cost due to model fine-tuning during inference. Most mask-propagation based models are faster but with relatively low performance due to failure to adapt to object appearance variation. In this paper, we are aiming to design a new model to make a good balance between speed and performance. We propose a model, called NPMCA-net, which directly localizes foreground objects based on mask-propagation and non-local technique by matching pixels in reference and target frames. Since we bring in information of both first and previous frames, our network is robust to large object appearance variation, and can better adapt to occlusions. Extensive experiments show that our approach can achieve a new state-of-the-art performance with a fast speed at the same time (86.5% IoU on DAVIS-2016 and 72.2% IoU on DAVIS-2017, with speed of 0.11s per frame) under the same level comparison. Source code is available at https://github.com/siyueyu/NPMCA-net.