2 citations · 4 across the 8 of their papers we have counts for
7 papers · 1 filter
Function-Space Temporal Self-Distillation for Policy Optimization
Aryan Das, Arnab Bhattacharya, Vinay Kumar Verma
Reinforcement Learning with Verifiable Rewards (RLVR) improves reasoning in language models, but scalar outcomes provide coarse supervision over response trajectories. Self-distill…
SALD: Self-Referenced Advantage Learning for Diffusion Models
Aryan Das, Surjo Dey, Koushik Biswas +4
Recent work on language-model adaptation has shown that single models can obtain informative training signals by evaluating their behavior in demonstrationor feedback-augmented con…
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…
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…
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…
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…
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…