MulViT-TF uses distributed multi-view vision and Transformer fusion to estimate RSSI, cutting RMSE by up to 26.3% versus single-view baselines in two indoor scenes while using fewer resources.
Decoupled weight decay regularization,
2 Pith papers cite this work. Polarity classification is still indexing.
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A frozen DINOv3 ViT-L/16 with AnyUp upsampling and lightweight CenterNet heads achieves 0.893 F1 and 1.41 mm localization error on arrow punctures using 48 training images.
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Distributed Multi-View Vision-Only RSSI Estimation
MulViT-TF uses distributed multi-view vision and Transformer fusion to estimate RSSI, cutting RMSE by up to 26.3% versus single-view baselines in two indoor scenes while using fewer resources.
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Frozen Vision Transformers for Dense Prediction on Small Datasets: A Case Study in Arrow Localization
A frozen DINOv3 ViT-L/16 with AnyUp upsampling and lightweight CenterNet heads achieves 0.893 F1 and 1.41 mm localization error on arrow punctures using 48 training images.