# Pixel-Level Bijective Matching for Video Object Segmentation arXiv: https://arxiv.org/abs/2110.01644 ยท HTML: https://mlsanity.com/paper/2110.01644/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Builds on 36 of its 47 references that are in the map. - First posted on arXiv 4 Oct 2021. ## Builds on (most cited first, 30 of 36) - [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) - [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015) - [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016) - [The 2017 DAVIS Challenge on Video Object Segmentation](https://mlsanity.com/paper/1704.00675/) (2017) - [Video Object Segmentation using Space-Time Memory Networks](https://mlsanity.com/paper/1904.00607/) (2019) - [YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark](https://mlsanity.com/paper/1809.03327/) (2018) - [An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution](https://mlsanity.com/paper/1807.03247/) (2018) - [YouTube-VOS: Sequence-to-Sequence Video Object Segmentation](https://mlsanity.com/paper/1809.00461/) (2018) - [The Secrets of Salient Object Segmentation](https://mlsanity.com/paper/1406.2807/) (2014) - [FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation](https://mlsanity.com/paper/1902.09513/) (2019) - [Collaborative Video Object Segmentation by Foreground-Background Integration](https://mlsanity.com/paper/2003.08333/) (2020) - [Video Object Segmentation with Episodic Graph Memory Networks](https://mlsanity.com/paper/2007.07020/) (2020) - [Kernelized Memory Network for Video Object Segmentation](https://mlsanity.com/paper/2007.08270/) (2020) - [SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation](https://mlsanity.com/paper/2101.08833/) (2021) - [Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement](https://mlsanity.com/paper/2010.07958/) (2020) - [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) - [VideoMatch: Matching based Video Object Segmentation](https://mlsanity.com/paper/1809.01123/) (2018) - [Learning What to Learn for Video Object Segmentation](https://mlsanity.com/paper/2003.11540/) (2020) - [RVOS: End-to-End Recurrent Network for Video Object Segmentation](https://mlsanity.com/paper/1903.05612/) (2019) - [Efficient Regional Memory Network for Video Object Segmentation](https://mlsanity.com/paper/2103.12934/) (2021) - [PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation](https://mlsanity.com/paper/1807.09190/) (2018) - [Learning Fast and Robust Target Models for Video Object Segmentation](https://mlsanity.com/paper/2003.00908/) (2020) - [A Generative Appearance Model for End-to-end Video Object Segmentation](https://mlsanity.com/paper/1811.11611/) (2018) - [Fast Video Object Segmentation using the Global Context Module](https://mlsanity.com/paper/2001.11243/) (2020) - [Learning Position and Target Consistency for Memory-based Video Object Segmentation](https://mlsanity.com/paper/2104.04329/) (2021) - [RANet: Ranking Attention Network for Fast Video Object Segmentation](https://mlsanity.com/paper/1908.06647/) (2019) - [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) ## Built on by (most cited first, 11 of 11) - [Putting the Object Back into Video Object Segmentation](https://mlsanity.com/paper/2310.12982/) (2023) - [Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2209.03138/) (2022) - [Unsupervised Video Object Segmentation via Prototype Memory Network](https://mlsanity.com/paper/2209.03712/) (2022) - [Leveraging Action Affinity and Continuity for Semi-supervised Temporal Action Segmentation](https://mlsanity.com/paper/2207.08653/) (2022) - [Tackling Background Distraction in Video Object Segmentation](https://mlsanity.com/paper/2207.06953/) (2022) - [MotionDeltaCNN: Sparse CNN Inference of Frame Differences in Moving Camera Videos](https://mlsanity.com/paper/2210.09887/) (2022) - [One-Shot Video Inpainting](https://mlsanity.com/paper/2302.14362/) (2023) - [Find First, Track Next: Decoupling Identification and Propagation in Referring Video Object Segmentation](https://mlsanity.com/paper/2503.03492/) (2025) - [Pixel-Level Equalized Matching for Video Object Segmentation](https://mlsanity.com/paper/2209.03139/) (2022) - [Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2309.14786/) (2023) - [DepthFlow: Exploiting Depth-Flow Structural Correlations for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2507.19790/) (2025) ## Abstract (arXiv) Semi-supervised video object segmentation (VOS) aims to track the designated objects present in the initial frame of a video at the pixel level. To fully exploit the appearance information of an object, pixel-level feature matching is widely used in VOS. Conventional feature matching runs in a surjective manner, i.e., only the best matches from the query frame to the reference frame are considered. Each location in the query frame refers to the optimal location in the reference frame regardless of how often each reference frame location is referenced. This works well in most cases and is robust against rapid appearance variations, but may cause critical errors when the query frame contains background distractors that look similar to the target object. To mitigate this concern, we introduce a bijective matching mechanism to find the best matches from the query frame to the reference frame and vice versa. Before finding the best matches for the query frame pixels, the optimal matches for the reference frame pixels are first considered to prevent each reference frame pixel from being overly referenced. As this mechanism operates in a strict manner, i.e., pixels are connected if and only if they are the sure matches for each other, it can effectively eliminate background distractors. In addition, we propose a mask embedding module to improve the existing mask propagation method. By embedding multiple historic masks with coordinate information, it can effectively capture the position information of a target object.