An end-to-end spiking encoder-decoder network achieves 92.05/87.04/86.51 AP on KITTI BEV detection with a claimed 3.33x synaptic energy reduction versus an equivalent CNN.
Focal loss for dense object detection
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Controlled tests on LoveDA and ISPRS Potsdam show visual SSM encoders deliver favorable speed-accuracy trade-offs but suffer most from boundary errors under domain shift, indicating that robustness and boundary-aware decoding will matter more than intra-family encoder scaling.
AMDD achieves 99.7% balanced accuracy and 99.8% AUC on FakeAVCeleb by using cross-modal forensic fingerprint consistency loss to align generator-specific artifacts across modalities while also reporting 95.9% attribution accuracy.
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.
citing papers explorer
-
Neuromorphic LiDAR-based Bird's Eye View Object Detection using Energy-efficient Spiking Neural Networks
An end-to-end spiking encoder-decoder network achieves 92.05/87.04/86.51 AP on KITTI BEV detection with a claimed 3.33x synaptic energy reduction versus an equivalent CNN.
-
A Controlled Benchmark of Visual State-Space Backbones with Domain-Shift and Boundary Analysis for Remote-Sensing Segmentation
Controlled tests on LoveDA and ISPRS Potsdam show visual SSM encoders deliver favorable speed-accuracy trade-offs but suffer most from boundary errors under domain shift, indicating that robustness and boundary-aware decoding will matter more than intra-family encoder scaling.
-
Attribution-Guided Multimodal Deepfake Detection via Cross-Modal Forensic Fingerprints
AMDD achieves 99.7% balanced accuracy and 99.8% AUC on FakeAVCeleb by using cross-modal forensic fingerprint consistency loss to align generator-specific artifacts across modalities while also reporting 95.9% attribution accuracy.
-
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.