Cross-Domain Transfer of Symbolic Reasoning for Outlier and Novelty Detection

While outlier detection in vision (Tian et al., 2024; Ayadi et al., 2017) is typically data-driven, other fields like NLP use explicit symbolic rules and knowledge graphs to flag anomalies. Inspired by the “transfer of conceptualizations” heuristic, this idea proposes encoding visual scene understanding as symbolic representations (e.g., objects, relations, actions), applying symbolic anomaly detection (borrowed from NLP or graph analytics) to flag “impossible” or highly improbable visual configurations (e.g., a car floating in the sky), and integrating this symbolic layer with deep learning for hybrid detection. Such a system could catch not just statistical outliers but also “semantic outliers” that defy world knowledge, which is crucial for autonomous driving, robotics, and medical imaging. The innovation is in fusing symbolic and data-driven anomaly detection, a rarely explored synergy in computer vision.

References:

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2. VOD: Vision-Based Building Energy Data Outlier Detection. Jinzhao Tian, Tianya Zhao, Zhuorui Li, Tian Li, Haipei Bie, Vivian Loftness (2024). Machine Learning and Knowledge Extraction.