3 citations · 4 across the 8 of their papers we have counts for
8 papers
LoRA-X: Bridging Foundation Models with Training-Free Cross-Model Adaptation
Farzad Farhadzadeh, Debasmit Das, Shubhankar Borse +1
The rising popularity of large foundation models has led to a heightened demand for parameter-efficient fine-tuning methods, such as Low-Rank Adaptation (LoRA), which offer perform…
FouRA: Fourier Low Rank Adaptation
Shubhankar Borse, Shreya Kadambi, Nilesh Prasad Pandey +7
While Low-Rank Adaptation (LoRA) has proven beneficial for efficiently fine-tuning large models, LoRA fine-tuned text-to-image diffusion models lack diversity in the generated imag…
PosSAM: Panoptic Open-vocabulary Segment Anything
Vibashan VS, Shubhankar Borse, Hyojin Park +4
In this paper, we introduce an open-vocabulary panoptic segmentation model that effectively unifies the strengths of the Segment Anything Model (SAM) with the vision-language CLIP…
Multi-camera Bird's Eye View Perception for Autonomous Driving
David Unger, Nikhil Gosala, Varun Ravi Kumar +3
Most automated driving systems comprise a diverse sensor set, including several cameras, Radars, and LiDARs, ensuring a complete 360°coverage in near and far regions. Unlike Radar…
X-Align++: cross-modal cross-view alignment for Bird's-eye-view segmentation
Shubhankar Borse, Senthil Yogamani, Marvin Klingner +4
Bird's-eye-view (BEV) grid is a typical representation of the perception of road components, e.g., drivable area, in autonomous driving. Most existing approaches rely on cameras on…
DejaVu: Conditional Regenerative Learning to Enhance Dense Prediction
Shubhankar Borse, Debasmit Das, Hyojin Park +3
We present DejaVu, a novel framework which leverages conditional image regeneration as additional supervision during training to improve deep networks for dense prediction tasks su…