LQE is a physics-constrained learnable dimensionality reduction technique that improves average mIoU in hyperspectral urban segmentation on three datasets while using only 12-36 parameters.
Eca-net: Efficient channel attention for deep convolutional neural networks
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PointCRA reduces information loss in deep point cloud networks by treating temporal trend variation as an extra evaluation dimension alongside spatial and channel attention, guided by a neighborhood homogeneity constraint.
A keypoint-based pipeline extracts and tracks points from event streams to compute accurate 6-DoF poses of moving objects, outperforming prior event-based methods in simulated and real tests.
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Learnable Quantum Efficiency Filters for Urban Hyperspectral Segmentation
LQE is a physics-constrained learnable dimensionality reduction technique that improves average mIoU in hyperspectral urban segmentation on three datasets while using only 12-36 parameters.
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Channel-Level Relation to Attentive Aggregation with Neighborhood-Homogeneity Constraint for Point Cloud Analysis
PointCRA reduces information loss in deep point cloud networks by treating temporal trend variation as an extra evaluation dimension alongside spatial and channel attention, guided by a neighborhood homogeneity constraint.
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Keypoint-based Dynamic Object 6-DoF Pose Tracking via Event Camera
A keypoint-based pipeline extracts and tracks points from event streams to compute accurate 6-DoF poses of moving objects, outperforming prior event-based methods in simulated and real tests.