2 citations · 4 across the 25 of their papers we have counts for
10 papers · 1 filter
The Accretion Explorer Interferometer (AEI) Phase I NASA Innovative Advanced Concepts Final Report
Kimberly A. Weaver, Jenna M. Cann, Kenneth Carpenter +30
We must create superb X-ray images to understand the detailed physical processes behind some of the most powerful astronomical objects. The need to achieve this capability has been…
DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning
Mainak Singha, Niccolò Biondi, Elisa Ricci +1
Multimodal Large Language Models (MLLMs) have shown strong multimodal instruction-following ability, but adapting them to diverse visual-language domains typically assumes centrali…
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…
CLIPoint3D: Language-Grounded Few-Shot Unsupervised 3D Point Cloud Domain Adaptation
Mainak Singha, Sarthak Mehrotra, Paolo Casari +3
Recent vision-language models (VLMs) such as CLIP demonstrate impressive cross-modal reasoning, extending beyond images to 3D perception. Yet, these models remain fragile under dom…
The Need for Ultra High Resolution X-ray Imaging
Kimberly A. Weaver, Jenna M. Cann, Ryan Pfeifle +20
This paper discusses the broad science case for obtaining milliarcsecond to microarcsecond astronomical imaging resolution in the soft to medium-energy X-ray band (~0.5 to ~8 keV).…
bi-modal textual prompt learning for vision-language models in remote sensing
Pankhi Kashyap, Mainak Singha, Biplab Banerjee
Prompt learning (PL) has emerged as an effective strategy to adapt vision-language models (VLMs), such as CLIP, for downstream tasks under limited supervision. While PL has demonst…
MMLGNet: Cross-Modal Alignment of Remote Sensing Data using CLIP
Aditya Chaudhary, Sneha Barman, Mainak Singha +3
In this paper, we propose a novel multimodal framework, Multimodal Language-Guided Network (MMLGNet), to align heterogeneous remote sensing modalities like Hyperspectral Imaging (H…
SDHSI-Net: Learning Better Representations for Hyperspectral Images via Self-Distillation
Prachet Dev Singh, Shyamsundar Paramasivam, Sneha Barman +4
Hyperspectral image (HSI) classification presents unique challenges due to its high spectral dimensionality and limited labeled data. Traditional deep learning models often suffer…
Reconstruction Guided Few-shot Network For Remote Sensing Image Classification
Mohit Jaiswal, Naman Jain, Shivani Pathak +4
Few-shot remote sensing image classification is challenging due to limited labeled samples and high variability in land-cover types. We propose a reconstruction-guided few-shot net…