most citedGW240925 and GW250207: Astrophysical Calibration of Gravitational-wave Detectors

2 citations · 2 across the 12 of their papers we have counts for

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cs.CV2026

GeoDisaster: Benchmarking Orchestrated Agents for Operational Disaster Geo-Intelligence

Maram Hasan, Aman Verma, Savitra Roy +5

Remote-sensing vision-language models (RS-VLMs) have advanced Earth-observation analysis toward visual interpretation and instruction-following, yet fall short of operational geo-i…

cs.CV2026

CR-JEPA: Cross-Modal Joint-Embedding Predictive Learning for Remote Sensing Image Retrieval

Md Aminur Hossain, Ayush V. Patel, Nitant Dube +1

Cross-modal remote sensing image retrieval aims to retrieve semantically related scenes across heterogeneous sensing modalities. This remains challenging because paired observation…

cs.CV2026

HQ-JEPA: Hybrid Quantum Joint-Embedding Predictive Architecture for Cross-Modal Remote Sensing Representation Learning

Md Aminur Hossain, Ayush V. Patel, Sanjay K. Singh +1

We introduce HQ-JEPA, a hybrid quantum-classical joint-embedding predictive architecture for cross-modal remote sensing representation learning. The proposed framework extends JEPA…

cs.CV2026

ArcGate: Adaptive Arctangent Gated Activation

Avik Bhattacharya, Siddhant Dnyanesh Gole, Subhasis Chaudhuri +2

Activation functions are central to deep networks, influencing non-linearity, feature learning, convergence, and robustness. This paper proposes the Adaptive Arctangent Gated Activ…

cs.CV2026

Spatial-Frequency Gated Swin Transformer for Remote Sensing Single-Image Super-Resolution

Md Aminur Hossain, Parekh Valkesh, Ayush V. Patel +3

Remote Sensing (RS) single-image super-resolution aims to reconstruct high-resolution imagery from low-resolution observations while preserving fine spatial structures. Recent Swin…

cs.CV2026

HQ-UNet: A Hybrid Quantum-Classical U-Net with a Quantum Bottleneck for Remote Sensing Image Segmentation

Md Aminur Hossain, Ayush V. Patel, Ikshwaku Vanani +1

Semantic segmentation in remote sensing is commonly addressed using classical deep learning architectures such as U-Net, which require a large number of parameters to model complex…