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        TITLE = "Multiple Instance Learning Convolutional Neural Networks for
Fine-Grained Aircraft Recognition",
        JOURNAL = RS,
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@article{bb156301,
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        TITLE = "Multiple Instance Learning with Genetic Pooling for medical data
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        TITLE = "A Remote-Sensing Scene-Image Classification Method Based on Deep
Multiple-Instance Learning with a Residual Dense Attention ConvNet",
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        BIBSOURCE = "http://www.visionbib.com/bibliography/pattern645mil1.html#TT152184"}

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        TITLE = "A Dual-Path Multiple Instance Learning Network Guided by Image
Quality Assessment for Cervical Whole Slide Image Classification",
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Imbalanced Classification in Pathology",
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@article{bb156351,
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@article{bb156352,
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@article{bb156353,
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@article{bb156355,
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@article{bb156356,
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@article{bb156357,
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        PAGES = "2209-2223",
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@article{bb156358,
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@article{bb156359,
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@article{bb156360,
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        JOURNAL = PAMI,
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        PAGES = "469-485",
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        AUTHOR = "Peng, X.K. and Wei, Y. and Deng, A.D. and Wang, D. and Hu, D.",
        TITLE = "Balanced Multimodal Learning via On-the-fly Gradient Modulation",
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@article{bb156362,
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        TITLE = "RoMo: Robust Unsupervised Multimodal Learning With Noisy Pseudo
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@inproceedings{bb156363,
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        TITLE = "ROLL: Robust Noisy Pseudo-label Learning for Multi-View Clustering
with Noisy Correspondence",
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        PAGES = "30732-30741",
        BIBSOURCE = "http://www.visionbib.com/bibliography/pattern645mmod2.html#TT152245"}

@inproceedings{bb156364,
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Information: Theory, Algorithm and Metric",
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@article{bb156365,
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        TITLE = "Robust Multimodal Learning With Missing Modalities via
Parameter-Efficient Adaptation",
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@article{bb156366,
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        TITLE = "Parameter-Free Deep Multi-Modal Clustering With Reliable Contrastive
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@article{bb156367,
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        TITLE = "Self-Supervised Multimodal Learning: A Survey",
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@article{bb156368,
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@article{bb156369,
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        TITLE = "Self-Balancing Multimodal Models via Multi-Loss Gradient Modulation",
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@article{bb156370,
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        TITLE = "Multimodal Remote Sensing Image Clustering on Superpixel Manifolds",
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@inproceedings{bb156371,
        AUTHOR = "Das, A.M. and Bhatt, G. and Kumari, L. and Verma, S. and Bilmes, J.",
        TITLE = "COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation",
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        BIBSOURCE = "http://www.visionbib.com/bibliography/pattern645mmod2.html#TT152253"}

@inproceedings{bb156372,
        AUTHOR = "Huang, C.X. and Wei, Y. and Yang, Z.Q. and Hu, D.",
        TITLE = "Adaptive Unimodal Regulation for Balanced Multimodal Information
Acquisition",
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@inproceedings{bb156373,
        AUTHOR = "Jiang, Y.C. and Fu, J. and Hao, C. and Hu, X.T. and Peng, Y.Z. and Geng, X. and Yang, X.",
        TITLE = "Mimic In-Context Learning for Multimodal Tasks",
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@inproceedings{bb156374,
        AUTHOR = "Zhao, Y.Z. and Liu, F. and Liu, Y. and Liao, M.X. and Gong, C. and Ye, Q.X. and Wan, F.",
        TITLE = "DynRefer: Delving into Region-level Multimodal Tasks via Dynamic
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        BIBSOURCE = "http://www.visionbib.com/bibliography/pattern645mmod2.html#TT152256"}

@inproceedings{bb156375,
        AUTHOR = "Mei, S.B. and Wang, H. and Ni, B.B.",
        TITLE = "GeoMM: On Geodesic Perspective for Multi-modal Learning",
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@inproceedings{bb156376,
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        TITLE = "Generative Modeling of Class Probability for Multi-Modal
Representation Learning",
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@inproceedings{bb156377,
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        TITLE = "M3dbench: Towards Omni 3d Assistant with Interleaved Multi-modal
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@inproceedings{bb156378,
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@inproceedings{bb156379,
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@inproceedings{bb156381,
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        YEAR = "2024",
        PAGES = "LXIV: 164-182",
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@inproceedings{bb156382,
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@inproceedings{bb156383,
        AUTHOR = "Wei, S. and Luo, Y. and Wang, Y. and Luo, C.",
        TITLE = "Robust Multimodal Learning via Representation Decoupling",
        BOOKTITLE = ECCV24,
        YEAR = "2024",
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@inproceedings{bb156384,
        AUTHOR = "Wei, Y. and Li, S.W. and Feng, R.X. and Hu, D.",
        TITLE = "Diagnosing and Re-learning for Balanced Multimodal Learning",
        BOOKTITLE = ECCV24,
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@inproceedings{bb156385,
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        TITLE = "Missing Modality Prediction for Unpaired Multimodal Learning via Joint
Embedding of Unimodal Models",
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