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JEDI: JEPA-to-Edge Distillation for Efficient Cropland Segmentation from Satellite Imagery
Kishor Kumar Bhaumik, Nicolas Roque dos Santos, Jia Chen +1
Large vision models provide useful representations for remote-sensing segmentation but are often too expensive for deployment at the satellite or field edge. Existing feature-level…
AD-SAM: Adapting the Segment Anything Model for Semantic Segmentation in Autonomous Driving
Mario Camarena, Het Patel, Fatemeh Nazari +3
This paper presents the Autonomous Driving Segment Anything Model (AD-SAM), a foundation-model adaptation framework for semantic segmentation in autonomous driving. AD-SAM combines…
Robust Vision-Language Models via Tensor Decomposition: A Defense Against Adversarial Attacks
Het Patel, Muzammil Allie, Qian Zhang +2
Vision language models (VLMs) excel in multimodal understanding but are prone to adversarial attacks. Existing defenses often demand costly retraining or significant architecture c…
Can SAM recognize crops? Quantifying the zero-shot performance of a semantic segmentation foundation model on generating crop-type maps using satellite imagery for precision agriculture
Rutuja Gurav, Het Patel, Zhuocheng Shang +4
Climate change is increasingly disrupting worldwide agriculture, making global food production less reliable. To tackle the growing challenges in feeding the planet, cutting-edge m…
Deepfake Representation with Multilinear Regression
Sara Abdali, M. Alex O. Vasilescu, Evangelos E. Papalexakis
Generative neural network architectures such as GANs, may be used to generate synthetic instances to compensate for the lack of real data. However, they may be employed to create m…