POST uses prior-observation adversarial learning on adjacency matrices to reduce spatial over-generalization in graph-based multivariate time series anomaly detection and achieves new SOTA results on detection and channel-wise localization.
Cbam: Convolutional block attention module
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
DINORANKCLIP outperforms CLIP and RANKCLIP on fine-grained and out-of-distribution tasks by injecting DINOv3 local structure and using third-order ranking consistency trained on Conceptual Captions 3M.
A 1-D CNN with novel multi-stage spectral attention mechanisms and adjustable class-balanced focal loss improves recognition accuracy on real ship-radiated noise datasets.
HTC-SGA Former is a hybrid CNN-Transformer model with MS-GLWA, SGFA, and BWACL that outperforms 14 prior methods on private coronary DSA datasets using only 0.81M parameters.
citing papers explorer
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POST: Prior-Observation Adversarial Learning of Spatio-Temporal Associations for Multivariate Time Series Anomaly Detection
POST uses prior-observation adversarial learning on adjacency matrices to reduce spatial over-generalization in graph-based multivariate time series anomaly detection and achieves new SOTA results on detection and channel-wise localization.
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DINORANKCLIP: DINOv3 Distillation and Injection for Vision-Language Pretraining with High-Order Ranking Consistency
DINORANKCLIP outperforms CLIP and RANKCLIP on fine-grained and out-of-distribution tasks by injecting DINOv3 local structure and using third-order ranking consistency trained on Conceptual Captions 3M.
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Modulation Feature Enhancement with a Multi-Stage Attention Network for Underwater Acoustic Target Recognition
A 1-D CNN with novel multi-stage spectral attention mechanisms and adjustable class-balanced focal loss improves recognition accuracy on real ship-radiated noise datasets.
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HTC-SGA Former: A Hybrid Transformer-CNN Network with Self-Guided Attention and a New Boundary-Weighted Adaptive Loss for Coronary DSA Vessel Segmentation
HTC-SGA Former is a hybrid CNN-Transformer model with MS-GLWA, SGFA, and BWACL that outperforms 14 prior methods on private coronary DSA datasets using only 0.81M parameters.