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[TPAMI] JointFormer: A Unified Framework with Joint Modeling for Video Object Segmentation

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JointFormer

The official PyTorch implementation of our paper:

[TPAMI 2025] JointFormer: A Unified Framework with Joint Modeling for Video Object Segmentation

Authors: Jiaming Zhang, Yutao Cui, Gangshan Wu, Limin Wang

Paper: arxiv, IEEE

Overview

A unified VOS framework for joint modeling the three elements of feature, correspondence, and our presented compressed memory.

Quick Start

  • See INSTALL.md for instructions of installing required python packages.
  • See DATASET.md for datasets download and preparation.
  • See TRAINING.md for training details.
  • See INFERENCE.md for inference details and downloading pretrained models.

Visualizations

Acknowledgements

This project is built upon XMem, ConvMAE. Thanks to the contributors of these great codebases.

Citation

@ARTICLE{10949703,
  author={Zhang, Jiaming and Cui, Yutao and Wu, Gangshan and Wang, Limin},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={JointFormer: A Unified Framework with Joint Modeling for Video Object Segmentation}, 
  year={2025},
  volume={},
  number={},
  pages={1-17},
  keywords={Feature extraction;Transformers;Pipelines;Object segmentation;Data mining;Benchmark testing;Correlation;Computer vision;Aggregates;Video sequences;Video object segmentation;joint modeling;compressed memory;vision transformer},
  doi={10.1109/TPAMI.2025.3557841}
}

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[TPAMI] JointFormer: A Unified Framework with Joint Modeling for Video Object Segmentation

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