Biplab Banerjee

Pretrained biomedical vision–language models (VLMs) such as BioMedCLIP perform well on average but often degrade on challenging modalities where inter-class margins are small and acquisition-specific variations are pronounced, especially under few-shot supervision and when modality priors differ...

Illustrating figurative language remains challenging due to its non-literal semantics, and existing text-to-image frameworks rely heavily on proprietary models or human supervision to achieve adequate alignment. We introduce CaRVE, a lightweight and fully open-source critique-driven framework tha...