3 papers
cs.CV2025
Strategic Base Representation Learning via Feature Augmentations for Few-Shot Class Incremental Learning
Parinita Nema, Vinod K Kurmi
Few-shot class incremental learning implies the model to learn new classes while retaining knowledge of previously learned classes with a small number of training instances. Existi…
cs.CV2025
Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation
Rini Smita Thakur, Vinod K. Kurmi
Semi-supervised (SS) semantic segmentation exploits both labeled and unlabeled images to overcome tedious and costly pixel-level annotation problems. Pseudolabel supervision is one…
cs.SD2024
Towards Robust Few-shot Class Incremental Learning in Audio Classification using Contrastive Representation
Riyansha Singh, Parinita Nema, Vinod K Kurmi
In machine learning applications, gradual data ingress is common, especially in audio processing where incremental learning is vital for real-time analytics. Few-shot class-increme…