% Key papers for Mask propagation for unsupervised video segmentation (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2019_see_14783,
  title = {{See More, Know More: Unsupervised Video Object Segmentation With Co-Attention Siamese Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2019_learning_14393,
  title = {{Learning Correspondence From the Cycle-Consistency of Time}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2018_efficient_13059,
  title = {{Efficient Video Object Segmentation via Network Modulation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Yang_Efficient_Video_Object_CVPR_2018_paper.html},
}

@inproceedings{cvpr2017_fusionseg_12296,
  title = {{FusionSeg: Learning to Combine Motion and Appearance for Fully Automatic Segmentation of Generic Objects in Videos}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{cvpr2018_cnn_12885,
  title = {{CNN in MRF: Video Object Segmentation via Inference in a CNN-Based Higher-Order Spatio-Temporal MRF}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Bao_CNN_in_MRF_CVPR_2018_paper.html},
}

@inproceedings{cvpr2021_sstvos_17811,
  title = {{SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2023_dropmae_20842,
  title = {{DropMAE: Masked Autoencoders With Spatial-Attention Dropout for Tracking Tasks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/22303},
}

@inproceedings{cvpr2020_learning_15789,
  title = {{Learning Video Object Segmentation From Unlabeled Videos}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2021_swiftnet_17965,
  title = {{SwiftNet: Real-Time Video Object Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2020_classifying_15251,
  title = {{Classifying, Segmenting, and Tracking Object Instances in Video with Mask Propagation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2018_monet_13376,
  title = {{MoNet: Deep Motion Exploitation for Video Object Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Xiao_MoNet_Deep_Motion_CVPR_2018_paper.html},
}

@inproceedings{neurips2021_rethinking_63444,
  title = {{Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27541},
}

@inproceedings{cvpr2020_a_15087,
  title = {{A Transductive Approach for Video Object Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2017_online_12517,
  title = {{Online Video Object Segmentation via Convolutional Trident Network}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{cvpr2016_track_11933,
  title = {{Track and Segment: An Iterative Unsupervised Approach for Video Object Proposals}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2016},
}

@inproceedings{cvpr2019_spatiotemporal_14857,
  title = {{Spatiotemporal CNN for Video Object Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2018_motion_13388,
  title = {{Motion-Guided Cascaded Refinement Network for Video Object Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Hu_Motion-Guided_Cascaded_Refinement_CVPR_2018_paper.html},
}

@inproceedings{cvpr2016_primary_11771,
  title = {{Primary Object Segmentation in Videos via Alternate Convex Optimization of Foreground and Background Distributions}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2016},
}

@inproceedings{neurips2021_video_63294,
  title = {{Video Instance Segmentation using Inter-Frame Communication Transformers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27805},
}

@inproceedings{aaai2022_reliable_2635,
  title = {{Reliable Propagation-Correction Modulation for Video Object Segmentation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/02946-reliable-propagation-correction-modulation-for-video-object-segmentation/},
}

@inproceedings{cvpr2024_videocutler_25229,
  title = {{VideoCutLER: Surprisingly Simple Unsupervised Video Instance Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30297},
}

@inproceedings{aaai2024_generalizable_6111,
  title = {{Generalizable Fourier Augmentation for Unsupervised Video Object Segmentation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28295},
}

@inproceedings{cvpr2025_denver_25865,
  title = {{DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34734},
}

@inproceedings{aaai2026_predicting_105566,
  title = {{Predicting Video Slot Attention Queries from Random Slot-Feature Pairs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/38322},
}

@inproceedings{iclr2024_vonet_35330,
  title = {{VONet: Unsupervised Video Object Learning With Parallel U-Net Attention and Object-wise Sequential VAE}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17743},
}

@inproceedings{icml2026_rethinking_52919,
  title = {{Rethinking Temporal Consistency in Video Object-Centric Learning: From Prediction to Correspondence}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=aH0jZGucwj},
}

@inproceedings{iclr2025_efficient_83695,
  title = {{Efficient Masked AutoEncoder for Video Object Counting and A Large-Scale Benchmark}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28124},
}

@inproceedings{neurips2026_s2d_93431,
  title = {{S2D: Sparse-To-Dense Keymask Distillation for Unsupervised Video Instance Segmentation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/153406},
}

@inproceedings{cvpr2026_towards_96420,
  title = {{Towards Stable Self-Supervised Object Representations in Unconstrained Egocentric Video}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Tan_Towards_Stable_Self-Supervised_Object_Representations_in_Unconstrained_Egocentric_Video_CVPR_2026_paper.html},
}

@inproceedings{cvpr2026_learning_96433,
  title = {{Learning Cross-View Object Correspondence via Cycle-Consistent Mask Prediction}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Yan_Learning_Cross-View_Object_Correspondence_via_Cycle-Consistent_Mask_Prediction_CVPR_2026_paper.html},
}

@inproceedings{cvpr2026_reconstruction_98429,
  title = {{Reconstruction-Guided Slot Curriculum: Addressing Object Over-Fragmentation in Video Object-Centric Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Moon_Reconstruction-Guided_Slot_Curriculum_Addressing_Object_Over-Fragmentation_in_Video_Object-Centric_Learning_CVPR_2026_paper.html},
}