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.

5 papers of 8,653Sort Recent · Most cited
  1. 2023
    Multi-Modal Continual Test-Time Adaptation for 3D Semantic SegmentationHaozhi Cao, Yuecong Xu, Jianfei Yang … Lihua XieICCV · Nanyang Technological University · Agency for Science, Technology and Research · +1
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  2. 2023
    Label-Guided Knowledge Distillation for Continual Semantic Segmentation on 2D Images and 3D Point CloudsZe Yang, Ruibo Li, Evan Ling … Guosheng LinICCV · Nanyang Technological University · Hyundai Motor Group (South Korea)
  3. 2023
    Label-Efficient Online Continual Object Detection in Streaming VideoJay Zhangjie Wu, David Junhao Zhang, Wynne Hsu … Mike Zheng ShouICCV · National University of Singapore · Agency for Science, Technology and Research · +1
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  4. 2023
    Learning to Learn: How to Continuously Teach Humans and MachinesParantak Singh, You Li, Ankur Sikarwar … Mengmi ZhangICCV · Agency for Science, Technology and Research · Nanyang Technological University · +5
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  5. 2021
    Else-Net: Elastic Semantic Network for Continual Action Recognition from Skeleton DataTianjiao Li, Qiuhong Ke, Hossein Rahmani … Jun LiuICCV · Singapore University of Technology and Design · The University of Melbourne · +2
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.