4 papers
BioVLM: Routing Prompts, Not Parameters, for Cross-Modality Generalization in Biomedical VLMs
Mainak Singha, Tanisha Gupta, Ankit Jha +3
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 a…
GeoMeld: Toward Semantically Grounded Foundation Models for Remote Sensing
Maram Hasan, Md Aminur Hossain, Savitra Roy +6
Effective foundation modeling in remote sensing requires spatially aligned heterogeneous modalities coupled with semantically grounded supervision, yet such resources remain limite…
FrogDogNet: Fourier frequency Retained visual prompt Output Guidance for Domain Generalization of CLIP in Remote Sensing
Hariseetharam Gunduboina, Muhammad Haris Khan, Biplab Banerjee
In recent years, large-scale vision-language models (VLMs) like CLIP have gained attention for their zero-shot inference using instructional text prompts. While these models excel…
OSLoPrompt: Bridging Low-Supervision Challenges and Open-Set Domain Generalization in CLIP
Mohamad Hassan N C, Divyam Gupta, Mainak Singha +4
We introduce Low-Shot Open-Set Domain Generalization (LSOSDG), a novel paradigm unifying low-shot learning with open-set domain generalization (ODG). While prompt-based methods usi…