Related work

The foundational work on continual learning, 1980 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

7 papers of 8,653Sort Recent · Most cited
  1. 2023
    CoMFormer: Continual Learning in Semantic and Panoptic SegmentationFabio Cermelli, Matthieu Cord, Arthur DouillardCVPR · Politecnico di Torino · Italian Institute of Technology · +2
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  2. 2022
    Incremental Learning in Semantic Segmentation from Image LabelsFabio Cermelli, Dario Fontanel, Antonio Tavera … Barbara CaputoCVPR · Politecnico di Torino · Italian Institute of Technology
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  3. 2022
    Modeling Missing Annotations for Incremental Learning in Object DetectionFabio Cermelli, Antonino Geraci, Dario Fontanel, Barbara CaputoCVPR · Politecnico di Torino · Italian Institute of Technology
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  4. 2022
    Modeling the Background for Incremental and Weakly-Supervised Semantic SegmentationFabio Cermelli, Massimiliano Mancini, Samuel Rota Bulo … Barbara CaputoTPAMI · Politecnico di Torino · TH Bingen University of Applied Sciences · +3
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  5. 2021
    Prototype-based Incremental Few-Shot SegmentationFabio Cermelli, Massimiliano Mancini, Yongqin Xian … Barbara CaputoBMVC
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  6. 2020
    Boosting Deep Open World Recognition by ClusteringDario Fontanel, Fabio Cermelli, Massimiliano Mancini … Barbara CaputoRA-L · Politecnico di Torino · Italian Institute of Technology · +4
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  7. 2020
    Modeling the Background for Incremental Learning in Semantic SegmentationFabio Cermelli, Massimiliano Mancini, Samuel Rota Bulò … Barbara CaputoCVPR · Politecnico di Torino · Italian Institute of Technology · +3
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About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. By default it shows the papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. The rest are one click away under “All papers”. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.