8 papers
Large Language Models Can Perform Automatic Modulation Classification via Discretized Self-supervised Candidate Retrieval
Mohammad Rostami, Atik Faysal, Reihaneh Gh. Roshan +3
Identifying wireless modulation schemes is essential for cognitive radio, but standard supervised models often degrade under distribution shift, and training domain-specific wirele…
CageDroneRF: A Large-Scale RF Benchmark and Toolkit for Drone Perception
Mohammad Rostami, Atik Faysal, Hongtao Xia +3
We present CageDroneRF (CDRF), a large-scale benchmark for Radio-Frequency (RF) drone detection and identification built from real-world captures and systematically generated synth…
Finetune-Informed Pretraining Boosts Downstream Performance
Atik Faysal, Mohammad Rostami, Reihaneh Gh. Roshan +2
Multimodal pretraining is effective for building general-purpose representations, but in many practical deployments, only one modality is heavily used during downstream fine-tuning…
Semi-Supervised Masked Autoencoders: Unlocking Vision Transformer Potential with Limited Data
Atik Faysal, Mohammad Rostami, Reihaneh Gh. Roshan +2
We address the challenge of training Vision Transformers (ViTs) when labeled data is scarce but unlabeled data is abundant. We propose Semi-Supervised Masked Autoencoder (SSMAE), a…
Plug-and-Play AMC: Context Is King in Training-Free, Open-Set Modulation with LLMs
Mohammad Rostami, Atik Faysal, Reihaneh Gh. Roshan +3
Automatic Modulation Classification (AMC) is critical for efficient spectrum management and robust wireless communications. However, AMC remains challenging due to the complex inte…
Meta-Task: A Method-Agnostic Framework for Learning to Regularize in Few-Shot Learning
Mohammad Rostami, Atik Faysal, Huaxia Wang +1
Overfitting is a significant challenge in Few-Shot Learning (FSL), where models trained on small, variable datasets tend to memorize rather than generalize to unseen tasks. Regular…