4 papers
Few-Shot Domain Incremental Learning via Continual Vision-Language Consolidation
Naeem Paeedeh, Mahardhika Pratama, Wolfgang Mayer +3
Existing domain-incremental learning (DIL) strategies call for massive amounts of data to adapt to new domains and suffer from the overfitting problem in the case of data scarcity.…
Cross-Domain Few-Shot Learning for Hyperspectral Image Classification Based on Mixup Foundation Model
Naeem Paeedeh, Mahardhika Pratama, Ary Shiddiqi +3
Although cross-domain few-shot learning (CDFSL) for hyper-spectral image (HSI) classification has attracted significant research interest, existing works often rely on an unrealist…
Cross-Domain Few-Shot Learning with Coalescent Projections and Latent Space Reservation
Naeem Paeedeh, Mahardhika Pratama, Imam Mustafa Kamal +3
Despite the progress in cross-domain few-shot learning, a model pre-trained with DINO combined with a prototypical classifier outperforms the latest SOTA methods. A crucial limitat…
Continual Knowledge Consolidation LORA for Domain Incremental Learning
Naeem Paeedeh, Mahardhika Pratama, Weiping Ding +4
Domain Incremental Learning (DIL) is a sub-branch of continual learning that aims to address the never-ending arrival of new domains without catastrophic forgetting. Despite the ad…