1 citations · 1 across the 2 of their papers we have counts for
7 papers
On the Physical Plausibility and Distribution Alignment for Sim-to-Real RF Positioning
Ararat Saribekyan, Armen Manukyan, Hrant Khachatrian +1
Reliable radio frequency (RF) positioning from cellular measurements is limited by the high cost and limited coverage of real drive-test data, especially when models must work on s…
On the Limitations of Ray-Tracing for Learning-Based RF Tasks in Urban Environments
Armen Manukyan, Hrant Khachatrian, Edvard Ghukasyan +1
We study the realism of Sionna v1.0.2 ray-tracing for outdoor cellular links in central Rome. We use a real measurement set of 1,664 user-equipments (UEs) and six nominal base-stat…
Fusion of Pervasive RF Data with Spatial Images via Vision Transformers for Enhanced Mapping in Smart Cities
Rafayel Mkrtchyan, Armen Manukyan, Hrant Khachatrian +1
In this paper, we present a deep learning-based approach that integrates the DINOv2 architecture to improve building mapping by combining (possibly erroneous) maps from open-source…
In-context Learning in Presence of Spurious Correlations
Hrayr Harutyunyan, Rafayel Darbinyan, Samvel Karapetyan +1
Large language models exhibit a remarkable capacity for in-context learning, where they learn to solve tasks given a few examples. Recent work has shown that transformers can be tr…
GeoCrossBench: Cross-Band Generalization for Remote Sensing
Hakob Tamazyan, Ani Vanyan, Alvard Barseghyan +3
The number and diversity of remote sensing satellites grows over time, while the vast majority of labeled data comes from older satellites. As the foundation models for Earth obser…
Do Satellite Tasks Need Special Pretraining?
Ani Vanyan, Alvard Barseghyan, Hakob Tamazyan +4
Foundation models have advanced machine learning across various modalities, including images. Recently multiple teams trained foundation models specialized for remote sensing appli…