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Few-Shot Class-Incremental Learning

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Showing 51–75 of 118 papers

TitleStatusHype
A Forward and Backward Compatible Framework for Few-shot Class-incremental Pill RecognitionCode0
PL-FSCIL: Harnessing the Power of Prompts for Few-Shot Class-Incremental LearningCode0
A streamlined Approach to Multimodal Few-Shot Class Incremental Learning for Fine-Grained DatasetsCode0
Foundation Model-Powered 3D Few-Shot Class Incremental Learning via Training-free AdaptorCode0
Few-Shot Class Incremental Learning via Robust Transformer ApproachCode0
Few-shot Tuning of Foundation Models for Class-incremental LearningCode0
Strategic Base Representation Learning via Feature Augmentations for Few-Shot Class Incremental Learning—0
Synthesized Feature Based Few-Shot Class-Incremental Learning on a Mixture of Subspaces—0
TLCE: Transfer-Learning Based Classifier Ensembles for Few-Shot Class-Incremental Learning—0
Towards Robust Few-shot Class Incremental Learning in Audio Classification using Contrastive Representation—0
Tripartite Weight-Space Ensemble for Few-Shot Class-Incremental Learning—0
Two-level Graph Network for Few-Shot Class-Incremental Learning—0
Inductive Graph Few-shot Class Incremental Learning—0
A Bag of Tricks for Few-Shot Class-Incremental Learning—0
Adaptive Additive Parameter Updates of Vision Transformers for Few-Shot Continual Learning—0
Aggregated f-average Neural Network for Interpretable Ensembling—0
AnchorInv: Few-Shot Class-Incremental Learning of Physiological Signals via Representation Space Guided Inversion—0
A New Benchmark for Few-Shot Class-Incremental Learning: Redefining the Upper Bound—0
An experimental approach on Few Shot Class Incremental Learning—0
A Survey on Few-Shot Class-Incremental Learning—0
Attraction Diminishing and Distributing for Few-Shot Class-Incremental Learning—0
Balanced Supervised Contrastive Learning for Few-Shot Class-Incremental Learning—0
Bias Mitigating Few-Shot Class-Incremental Learning—0
Brain-inspired analogical mixture prototypes for few-shot class-incremental learning—0
Breaking Fine-Grained Classification Barriers with Cost-Free Data in Few-Shot Class-Incremental Learning—0
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