2 citations · 4 across the 25 of their papers we have counts for
26 papers
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…
Detecting AI Hallucinations in Finance: An Information-Theoretic Method Cuts Hallucination Rate by 92%
Mainak Singha
Large language models (LLMs) produce fluent but unsupported answers - hallucinations - limiting safe deployment in high-stakes domains. We propose ECLIPSE, a framework that treats…
How (Mis)calibrated is your Federated CLIP and what to do about it?
Mainak Singha, Masih Aminbeidokhti, Paolo Casari +3
Vision-language models (VLMs) such as CLIP are increasingly adapted across decentralized data silos, yet the reliability of their predictions under federated learning (FL) remains…
Learning Under Laws: A Constraint-Projected Neural PDE Solver that Eliminates Hallucinations
Mainak Singha
Neural networks can approximate solutions to partial differential equations, but they often break the very laws they are meant to model-creating mass from nowhere, drifting shocks,…
FedMVP: Federated Multimodal Visual Prompt Tuning for Vision-Language Models
Mainak Singha, Subhankar Roy, Sarthak Mehrotra +4
In federated learning, textual prompt tuning adapts Vision-Language Models (e.g., CLIP) by tuning lightweight input tokens (or prompts) on local client data, while keeping network…
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…
GraphVL: Graph-Enhanced Semantic Modeling via Vision-Language Models for Generalized Class Discovery
Bhupendra Solanki, Ashwin Nair, Mainak Singha +3
Generalized Category Discovery (GCD) aims to cluster unlabeled images into known and novel categories using labeled images from known classes. To address the challenge of transferr…
COSMo: CLIP Talks on Open-Set Multi-Target Domain Adaptation
Munish Monga, Sachin Kumar Giroh, Ankit Jha +3
Multi-Target Domain Adaptation (MTDA) entails learning domain-invariant information from a single source domain and applying it to multiple unlabeled target domains. Yet, existing…
Elevating All Zero-Shot Sketch-Based Image Retrieval Through Multimodal Prompt Learning
Mainak Singha, Ankit Jha, Divyam Gupta +2
We address the challenges inherent in sketch-based image retrieval (SBIR) across various settings, including zero-shot SBIR, generalized zero-shot SBIR, and fine-grained zero-shot…
CDAD-Net: Bridging Domain Gaps in Generalized Category Discovery
Sai Bhargav Rongali, Sarthak Mehrotra, Ankit Jha +5
In Generalized Category Discovery (GCD), we cluster unlabeled samples of known and novel classes, leveraging a training dataset of known classes. A salient challenge arises due to…
Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential for Open Domain Generalization
Mainak Singha, Ankit Jha, Shirsha Bose +3
We delve into Open Domain Generalization (ODG), marked by domain and category shifts between training's labeled source and testing's unlabeled target domains. Existing solutions to…
C-SAW: Self-Supervised Prompt Learning for Image Generalization in Remote Sensing
Avigyan Bhattacharya, Mainak Singha, Ankit Jha +1
We focus on domain and class generalization problems in analyzing optical remote sensing images, using the large-scale pre-trained vision-language model (VLM), CLIP. While contrast…
HAVE-Net: Hallucinated Audio-Visual Embeddings for Few-Shot Classification with Unimodal Cues
Ankit Jha, Debabrata Pal, Mainak Singha +2
Recognition of remote sensing (RS) or aerial images is currently of great interest, and advancements in deep learning algorithms added flavor to it in recent years. Occlusion, intr…
GOPro: Generate and Optimize Prompts in CLIP using Self-Supervised Learning
Mainak Singha, Ankit Jha, Biplab Banerjee
Large-scale foundation models, such as CLIP, have demonstrated remarkable success in visual recognition tasks by embedding images in a semantically rich space. Self-supervised lear…
AD-CLIP: Adapting Domains in Prompt Space Using CLIP
Mainak Singha, Harsh Pal, Ankit Jha +1
Although deep learning models have shown impressive performance on supervised learning tasks, they often struggle to generalize well when the training (source) and test (target) do…