Author: Han-Pang Chiu
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Enabling Scalable Mineral Exploration: Self-Supervision and Explainability
Abstract Machine Learning (ML) for Mineral Prospectivity Mapping (MPM) is important for automating and accelerating the workflow to critical mineral assessment. Recent MPM works have explored Deep Learning (DL) as a modeling tool with more representation capacity. However, these overparameterized methods may be more prone to overfitting due to their reliance on scarce labeled data…
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GFM4MPM: Towards Geospatial Foundation Models for Mineral Prospectivity Mapping
Abstract Machine Learning (ML) for Mineral Prospectivity Mapping (MPM) remains a challenging problem as it requires the analysis of associations between large-scale multi-modal geospatial data and a few historical mineral commodity observations (positive labels). Recent MPM works have explored Deep Learning (DL) as a modeling tool with more representation capacity. However, these overparameterized methods may…
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SayNav: Grounding Large Language Models for Dynamic Planning to Navigation in New Environments
We present SayNav, a new approach that leverages human knowledge from Large Language Models (LLMs) for efficient generalization to complex navigation tasks in unknown large-scale environments.
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Machine Learning Aided GPS-Denied Navigation Using Uncertainty Estimation through Deep Neural Networks
We describe and demonstrate a novel approach for generating accurate and interpretable uncertainty estimation for outputs from a DNN in real time.
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Unsupervised Domain Adaptation for Semantic Segmentation with Pseudo Label Self-Refinement
We propose an auxiliary pseudo-label refinement network (PRN) for online refining of the pseudo labels and also localizing the pixels whose predicted labels are likely to be noisy.
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C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation
We propose C-SFDA, a curriculum learning aided self-training framework for SFDA that adapts efficiently and reliably to changes across domains based on selective pseudo-labeling. Specifically, we employ a curriculum learning scheme to promote learning from a restricted amount of pseudo labels selected based on their reliabilities.
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Night-Time GPS-Denied Navigation and Situational Understanding Using Vision-Enhanced Low-Light Imager
In this presentation, we describe and demonstrate a novel vision-enhanced low-light imager system to provide GPS-denied navigation and ML-based visual scene understanding capabilities for both day and night operations.
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Vision based Navigation using Cross-View Geo-registration for Outdoor Augmented Reality and Navigation Applications
In this work, we present a new vision-based cross-view geo-localization solution matching camera images to a 2D satellite/ overhead reference image database. We present solutions for both coarse search for cold start and fine alignment for continuous refinement.
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Cross-View Visual Geo-Localization for Outdoor Augmented Reality
We address the problem of geo-pose estimation by cross-view matching of query ground images to a geo-referenced aerial satellite image database. Recently, neural network-based methods have shown state-of-the-art performance in cross-view matching.
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Autonomous Docking Using Learning-Based Scene Segmentation in Underground Mine Environments
This paper describes a vision-based autonomous docking solution that moves a coalmine shuttle car to the continuous miner in GPS-denied underground environments.
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Ranging-Aided Ground Robot Navigation Using UWB Nodes at Unknown Locations
This paper describes a new ranging-aided navigation approach that does not require the locations of ranging radios.
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Incremental Learning with Differentiable Architecture and Forgetting Search
In this paper, we show that leveraging NAS for incremental learning results in strong performance gains for classification tasks.