123 citations · 212 across the 13 of their papers we have counts for
4 papers · 1 filter
TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data
Jeremy Andrew Irvin, Emily Ruoyu Liu, Joyce Chuyi Chen +5
Large vision and language assistants have enabled new capabilities for interpreting natural images. These approaches have recently been adapted to earth observation data, but they…
ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts
Samar Khanna, Medhanie Irgau, David B. Lobell +1
Parameter-efficient fine-tuning (PEFT) techniques such as low-rank adaptation (LoRA) can effectively adapt large pre-trained foundation models to downstream tasks using only a smal…
SpotNet: An Image Centric, Lidar Anchored Approach To Long Range Perception
Louis Foucard, Samar Khanna, Yi Shi +4
In this paper, we propose SpotNet: a fast, single stage, image-centric but LiDAR anchored approach for long range 3D object detection. We demonstrate that our approach to LiDAR/ima…
Large Language Models are Geographically Biased
Rohin Manvi, Samar Khanna, Marshall Burke +2
Large Language Models (LLMs) inherently carry the biases contained in their training corpora, which can lead to the perpetuation of societal harm. As the impact of these foundation…