Mahalanobis PatchCore adds covariance-aware whitening and incremental streaming aggregation to PatchCore, preserving benchmark performance while cutting peak memory from 5.41 GB to 2.78 GB and raising mean industrial AUC from 0.981 to 0.986.
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Pith papers citing it
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Introduces a non-causal attention refinement module to remove order dependence from cell representations in autoregressive table recognition models.
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Mahalanobis PatchCore: Covariance-Aware and Streaming-Compatible Industrial Anomaly Detection
Mahalanobis PatchCore adds covariance-aware whitening and incremental streaming aggregation to PatchCore, preserving benchmark performance while cutting peak memory from 5.41 GB to 2.78 GB and raising mean industrial AUC from 0.981 to 0.986.
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Revisiting Structural Dependency in Autoregressive Multi-Task Table Recognition via Order-Independent Cell-Level Representations
Introduces a non-causal attention refinement module to remove order dependence from cell representations in autoregressive table recognition models.