Papers
1–3 of 3Research Paper·Mar 9, 2026
Local-Global Prompt Learning via Sparse Optimal Transport
Few-shot adaptation of vision-language models (VLMs) like CLIP typically relies on learning textual prompts matched to global image embeddings. Recent works extend this paradigm by incorporating local...
8.0 viability
Research Paper·Mar 4, 2026
SPRINT: Semi-supervised Prototypical Representation for Few-Shot Class-Incremental Tabular Learning
Real-world systems must continuously adapt to novel concepts from limited data without forgetting previously acquired knowledge. While Few-Shot Class-Incremental Learning (FSCIL) is established in com...
7.0 viability
Research Paper·Mar 12, 2026
Noise-aware few-shot learning through bi-directional multi-view prompt alignment
Vision-language models offer strong few-shot capability through prompt tuning but remain vulnerable to noisy labels, which can corrupt prompts and degrade cross-modal alignment. Existing approaches st...
7.0 viability