Domain and prompt-based: research map

114 accepted papers on Domain and prompt-based in Continual learning, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 2 clusters and 4 approaches. The busiest year so far is 2024.

Within Continual learning, its share shrank from 12.5% in 2023–24 to 8.5% in 2025–26 (44 → 49 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).

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Approaches and key papers

domains · time adaptation · test time · 74 papers

Approaches in this cluster:

Domain-incremental learning with memory (49 papers)
Adapts to sequentially arriving domains using memory, domain-aware representations and randomization.
Continual test-time adaptation (25 papers)
Adapts models online to shifting target domains using adapters, prompts and knowledge fusion.

Most cited and most cited since 2024:

Continual Test-Time Domain Adaptation (CVPR 2022 · 484 citations)
Learn From Others and Be Yourself in Heterogeneous Federated Learning (CVPR 2022 · 279 citations)
Each Test Image Deserves A Specific Prompt: Continual Test-Time Adaptation for 2D Medical Image Segmentation (CVPR 2024 · 44 citations)
Multi-Domain Incremental Learning for Face Presentation Attack Detection (AAAI 2024 · 30 citations)

prompts · person · prompting · 40 papers