Memory-Bank Calibration for Localized Visual Novelty under Background Shift
Keywords:
Visual Novelty Detection, Memory Bank, Background Shift, Anomaly Localization, Dynamic CalibrationAbstract
The detection of localized visual novelty remains a critical challenge in computer vision, particularly when operating within unstructured environments characterized by significant background shift. Traditional anomaly detection models often assume a static background distribution, leading to a high rate of false positives when faced with varying illumination, weather conditions, or sensor perturbations. This paper introduces a comprehensive memory-bank calibration framework designed to decouple localized anomalous features from global environmental variations. By leveraging a dynamically updated feature repository, the proposed system continuously calibrates its reference distribution to reflect the current background state, thereby isolating genuine structural or semantic novelties. We detail the system architecture, which extracts deep hierarchical features and maps them into a spatially aware memory bank. Furthermore, a novel calibration mechanism is introduced that operates directly in the latent space, adjusting the stored normative representations based on contextual background cues without requiring computationally expensive retraining. Extensive theoretical analysis and qualitative evaluations demonstrate that this calibration approach significantly mitigates the impact of background shift, enhancing both the precision and robustness of novelty localization. The findings present a paradigm shift in how memory-augmented neural networks handle non-stationary environments, offering scalable solutions for industrial inspection, autonomous navigation, and environmental monitoring.
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Copyright (c) 2026 Alaa Hussein
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