The field of object detection and neural architecture search is moving towards more efficient and accurate models, particularly in the context of radar and Synthetic Aperture Radar (SAR) applications. Researchers are exploring new approaches to improve the robustness and performance of object detection models in adverse lighting and weather conditions. One of the key trends is the use of multi-representation and neural architecture search to optimize model performance and efficiency. Notable papers in this area include:

• Multi-Representation Adapter with Neural Architecture Search for Efficient Range-Doppler Radar Object Detection, which achieves state-of-the-art performance on RADDet and CARRADA datasets.
• SAR-NAS: Lightweight SAR Object Detection with Neural Architecture Search, which introduces NAS to SAR object detection for the first time and achieves superior detection accuracy while maintaining lower computational overhead.
• OptiProxy-NAS: Optimization Proxy based End-to-End Neural Architecture Search and LM-Searcher: Cross-domain Neural Architecture Search with LLMs via Unified Numerical Encoding, which propose novel frameworks for efficient and flexible neural architecture search.