2 citations · 5 across the 20 of their papers we have counts for
21 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…
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…
Two-Stage Vision Transformer for Image Restoration: Colorization Pretraining + Residual Upsampling
Aditya Chaudhary, Prachet Dev Singh, Ankit Jha
In computer vision, Single Image Super-Resolution (SISR) is still a difficult problem. We present ViT-SR, a new technique to improve the performance of a Vision Transformer (ViT) e…
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…
Foundation Models and Adaptive Feature Selection: A Synergistic Approach to Video Question Answering
Sai Bhargav Rongali, Mohamad Hassan N C, Ankit Jha +3
This paper tackles the intricate challenge of video question-answering (VideoQA). Despite notable progress, current methods fall short of effectively integrating questions with vid…
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…
In the Era of Prompt Learning with Vision-Language Models
Ankit Jha
Large-scale foundation models like CLIP have shown strong zero-shot generalization but struggle with domain shifts, limiting their adaptability. In our work, we introduce \textsc{S…
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…
Learning Class and Domain Augmentations for Single-Source Open-Domain Generalization
Prathmesh Bele, Valay Bundele, Avigyan Bhattacharya +3
Single-source open-domain generalization (SS-ODG) addresses the challenge of labeled source domains with supervision during training and unlabeled novel target domains during testi…
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…
APPLeNet: Visual Attention Parameterized Prompt Learning for Few-Shot Remote Sensing Image Generalization using CLIP
Mainak Singha, Ankit Jha, Bhupendra Solanki +2
In recent years, the success of large-scale vision-language models (VLMs) such as CLIP has led to their increased usage in various computer vision tasks. These models enable zero-s…
StyLIP: Multi-Scale Style-Conditioned Prompt Learning for CLIP-based Domain Generalization
Shirsha Bose, Ankit Jha, Enrico Fini +3
Large-scale foundation models, such as CLIP, have demonstrated impressive zero-shot generalization performance on downstream tasks, leveraging well-designed language prompts. Howev…