# Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1708.05137 · HTML: https://mlsanity.com/paper/1708.05137/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2017.

## Built on by (most cited first, 30 of 32)

- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [MOSE: A New Dataset for Video Object Segmentation in Complex Scenes](https://mlsanity.com/paper/2302.01872/) (2023)
- [A Survey on Deep Learning Technique for Video Segmentation](https://mlsanity.com/paper/2107.01153/) (2021)
- [LVOS: A Benchmark for Long-term Video Object Segmentation](https://mlsanity.com/paper/2211.10181/) (2022)
- [Hierarchical Memory Matching Network for Video Object Segmentation](https://mlsanity.com/paper/2109.11404/) (2021)
- [SwiftNet: Real-time Video Object Segmentation](https://mlsanity.com/paper/2102.04604/) (2021)
- [MOSEv2: A More Challenging Dataset for Video Object Segmentation in Complex Scenes](https://mlsanity.com/paper/2508.05630/) (2025)
- [Joint Inductive and Transductive Learning for Video Object Segmentation](https://mlsanity.com/paper/2108.03679/) (2021)
- [LVOS: A Benchmark for Large-scale Long-term Video Object Segmentation](https://mlsanity.com/paper/2404.19326/) (2024)
- [Local-Global Context Aware Transformer for Language-Guided Video Segmentation](https://mlsanity.com/paper/2203.09773/) (2022)
- [Active Boundary Loss for Semantic Segmentation](https://mlsanity.com/paper/2102.02696/) (2021)
- [Locality-Aware Inter-and Intra-Video Reconstruction for Self-Supervised Correspondence Learning](https://mlsanity.com/paper/2203.14333/) (2022)
- [EdgeTAM: On-Device Track Anything Model](https://mlsanity.com/paper/2501.07256/) (2025)
- [Region Aware Video Object Segmentation with Deep Motion Modeling](https://mlsanity.com/paper/2207.10258/) (2022)
- [Learning the What and How of Annotation in Video Object Segmentation](https://mlsanity.com/paper/2311.04414/) (2023)
- [Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild](https://mlsanity.com/paper/2103.10391/) (2021)
- [SiamMask: A Framework for Fast Online Object Tracking and Segmentation](https://mlsanity.com/paper/2207.02088/) (2022)
- [SimulFlow: Simultaneously Extracting Feature and Identifying Target for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2311.18286/) (2023)
- [Pixel-Level Bijective Matching for Video Object Segmentation](https://mlsanity.com/paper/2110.01644/) (2021)
- [Video Object Segmentation in Panoptic Wild Scenes](https://mlsanity.com/paper/2305.04470/) (2023)
- [Neural 3D Clothes Retargeting from a Single Image](https://mlsanity.com/paper/2102.00062/) (2021)
- [Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation](https://mlsanity.com/paper/2308.13266/) (2023)
- [Fast Pixel-Matching for Video Object Segmentation](https://mlsanity.com/paper/2107.04279/) (2021)
- [SiamPolar: Semi-supervised Realtime Video Object Segmentation with Polar Representation](https://mlsanity.com/paper/2110.14773/) (2021)
- [RGB-D Video Object Segmentation via Enhanced Multi-store Feature Memory](https://mlsanity.com/paper/2504.16471/) (2025)
- [Deep Learning Empowered Super-Resolution: A Comprehensive Survey and Future Prospects](https://mlsanity.com/paper/2509.22692/) (2025)
- [Robust Ego-Exo Correspondence with Long-Term Memory](https://mlsanity.com/paper/2510.11417/) (2025)
- [Attention based Memory video portrait matting](https://mlsanity.com/paper/2203.06890/) (2022)
- [Two-Stream Networks for Object Segmentation in Videos](https://mlsanity.com/paper/2208.04026/) (2022)
- [Reliability-Hierarchical Memory Network for Scribble-Supervised Video Object Segmentation](https://mlsanity.com/paper/2303.14384/) (2023)

## Abstract (arXiv)

We propose a novel video object segmentation algorithm based on pixel-level matching using Convolutional Neural Networks (CNN). Our network aims to distinguish the target area from the background on the basis of the pixel-level similarity between two object units. The proposed network represents a target object using features from different depth layers in order to take advantage of both the spatial details and the category-level semantic information. Furthermore, we propose a feature compression technique that drastically reduces the memory requirements while maintaining the capability of feature representation. Two-stage training (pre-training and fine-tuning) allows our network to handle any target object regardless of its category (even if the object's type does not belong to the pre-training data) or of variations in its appearance through a video sequence. Experiments on large datasets demonstrate the effectiveness of our model - against related methods - in terms of accuracy, speed, and stability. Finally, we introduce the transferability of our network to different domains, such as the infrared data domain.