A new class-adaptive fusion architecture improves multi-class LiDAR 3D object detection in V2X cooperative perception by routing small and large objects through attentive pathways and balancing training objectives.
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FADPNet decomposes facial features into low- and high-frequency components processed by dedicated Mamba and CNN modules to balance quality and efficiency in face super-resolution.
citing papers explorer
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Class-Adaptive Cooperative Perception for Multi-Class LiDAR-based 3D Object Detection in V2X Systems
A new class-adaptive fusion architecture improves multi-class LiDAR 3D object detection in V2X cooperative perception by routing small and large objects through attentive pathways and balancing training objectives.
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FADPNet: Frequency-Aware Dual-Path Network for Face Super-Resolution
FADPNet decomposes facial features into low- and high-frequency components processed by dedicated Mamba and CNN modules to balance quality and efficiency in face super-resolution.