most citedHyperCap: Hyperspectral Land Cover Captioning Dataset for Vision Language Models

1 citations · 1 across the 4 of their papers we have counts for

collaborators

6 papers

cs.LG2026

Source-Free Controlled Adaptation of Teachers for Continual Test-Time Adaptation

Anurag Roy, Riddhiman Moulick, Vinay Kumar Verma +2

In many real-world scenarios, encountering continual shifts in domain during inference is very common. Consequently, continual test-time adaptation (CTTA) techniques leveraging a t…

cs.CV2026

UNITY: Attention Flow Networks for Adaptive Conditioning in Diffusion

Aryan Das, Koushik Biswas, Moloud Abdar +1

We introduce UNITY, a Universal-to-Specialized adapter for efficient and scalable composite conditioning in diffusion based image generation. Unlike prior methods that train separa…

cs.CV2026

FOCUS: Forcing In-Context Object Localization through Visual Support Constraints and Policy Optimization

Mohammed Asad Karim, Vinay Kumar Verma

In-context localization (ICL) seeks to localize a target object specified by a small set of support examples in a query image, operating on the fly without training or parameter up…

cs.CV20261 cited

HyperCap: Hyperspectral Land Cover Captioning Dataset for Vision Language Models

Aryan Das, Tanishq Rachamalla, Pravendra Singh +5

We introduce HyperCap, the first large-scale hyperspectral captioning dataset designed to enhance model performance and effectiveness in remote sensing applications. Unlike traditi…

cs.CV2026

Efficient Text-Guided Convolutional Adapter for the Diffusion Model

Aryan Das, Koushik Biswas, Swalpa Kumar Roy +2

We introduce the Nexus Adapters, novel text-guided efficient adapters to the diffusion-based framework for the Structure Preserving Conditional Generation (SPCG). Recently, structu…

cs.CV2026

Uncertainty-Aware Vision-Language Segmentation for Medical Imaging

Aryan Das, Tanishq Rachamalla, Koushik Biswas +2

We introduce a novel uncertainty-aware multimodal segmentation framework that leverages both radiological images and associated clinical text for precise medical diagnosis. We prop…