BIAS is a biologically inspired video saliency model that integrates static and motion features via retina-like detection and multi-Gaussian fitting, outperforming baselines on DHF1K and anticipating traffic accidents up to 0.72 seconds early.
Designing BERT for convolutional networks: Sparse and hierarchical masked modeling
2 Pith papers cite this work. Polarity classification is still indexing.
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A new PCB defect detection method using structure-guided masked pretraining and spatial continuity regularization achieves 85.5% mAP0.5 on the DsPCBSD+ dataset.
citing papers explorer
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BIAS: A Biologically Inspired Algorithm for Video Saliency Detection
BIAS is a biologically inspired video saliency model that integrates static and motion features via retina-like detection and multi-Gaussian fitting, outperforming baselines on DHF1K and anticipating traffic accidents up to 0.72 seconds early.
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Structure-Guided Mixed Masked Pretraining and Spatial Continuity Regularization for Printed Circuit Board Defect Detection
A new PCB defect detection method using structure-guided masked pretraining and spatial continuity regularization achieves 85.5% mAP0.5 on the DsPCBSD+ dataset.