What is the trade-off between token sparsity levels (e.g., 10\%, 30\%, 50\%) in masked autoencoders and segmentat

Abstract

Abstract: In recent years unmanned aerial vehicles (UAVs) have emerged as a popular and cost-effective technology to capture high spatial and temporal resolution remote sensing (RS) images for a wide range of precision agriculture applications, which can help reduce costs and environmental impacts by providing detailed agricultural information to optimize field practices. Furthermore, deep learning (DL) has been successfully applied in agricultural applications such as weed detection, crop pest and disease detection, etc. as an intelligent tool. However, most DL-based methods place high computation, mem

Research Question

What is the trade-off between token sparsity levels (e.g., 10%, 30%, 50%) in masked autoencoders and segmentation accuracy for dynamic object classes (vehicles, pedestrians) in nuScenes videos across different inference hardware (A100 vs. Jetson Orin)?

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