Research map / Continual learning
Class-incremental: research map
303 accepted papers on Class-incremental in Continual learning, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 2 clusters and 6 approaches. The busiest year so far is 2026.
Within Continual learning, its share shrank from 26.2% in 2023–24 to 23.4% in 2025–26 (92 → 135 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).
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Approaches and key papers
shot · incremental cil · feature · 263 papers
Approaches in this cluster:
- Exemplar-efficient class-incremental learning (111 papers)
Retains old classes with compact exemplars, embedding distillation and pretrained models. - Class-balanced knowledge retention (73 papers)
Preserves per-class knowledge and decision boundaries when new classes arrive. - Few-shot class-incremental prototypes (47 papers)
Adds new classes from few samples using prototype calibration, meta-learning and orthogonal features. - Incremental object detection with replay (32 papers)
Reduces forgetting in detectors using distillation and generative replay of old classes.
Most cited and most cited since 2024:
- iCaRL: Incremental Classifier and Representation Learning (CVPR 2017 · 3,848 citations)
- Large Scale Incremental Learning (CVPR 2019 · 1,269 citations)
- Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning (CVPR 2024 · 117 citations)
- Gradient Reweighting: Towards Imbalanced Class-Incremental Learning (CVPR 2024 · 57 citations)
segmentation · semantic · background · 40 papers
Approaches in this cluster:
- Unknown-class mining for incremental segmentation (23 papers)
Models background shift by mining unseen classes and using exemplar or prototype replay. - Distillation-based continual segmentation (17 papers)
Preserves old classes in segmentation using similarity-weighted distillation, instance replay and memory selection.
Most cited and most cited since 2024:
- Modeling the Background for Incremental Learning in Semantic Segmentation (CVPR 2020 · 344 citations)
- PLOP: Learning Without Forgetting for Continual Semantic Segmentation (CVPR 2021 · 265 citations)
- Continual Segmentation with Disentangled Objectness Learning and Class Recognition (CVPR 2024 · 15 citations)
- Incremental Nuclei Segmentation from Histopathological Images via Future-class Awareness and Compatibility-inspired Distillation (CVPR 2024 · 11 citations)