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Beyond Visual Appearances: Privacy-sensitive Objects Identification via Hybrid Graph Reasoning

Created by
  • Haebom

Author

Zhuohang Jiang, Bingkui Tong, Xia Du, Ahmed Alhammadi, Jizhe Zhou

Outline

This paper proposes a PrivacyGuard framework to solve the Privacy-sensitive Object Identification (POI) problem by interpreting POI as a visual reasoning task to determine the privacy class of each object. PrivacyGuard comprises i) a Structuring step that constructs a heterogeneous scene graph rich in scene context, ii) a Data Augmentation step that proposes a contextual perturbation oversampling strategy to address the imbalance of privacy classes, and iii) a Hybrid Graph Generation & Reasoning step that generates a hybrid graph that allows direct message passing between nodes and edges to capture subtle contextual changes.

Takeaways, Limitations

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A novel approach to solving the POI problem by interpreting it as a visual reasoning problem.
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The PrivacyGuard framework leverages heterogeneous graphs, data augmentation, and hybrid graph structures to effectively utilize scene context.
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Addressing the privacy class imbalance problem through contextual perturbation oversampling strategy.
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Additional information is needed on specific hybrid graph-based inference results and performance.
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The effectiveness and computational cost of the PrivacyGuard framework need to be evaluated in practical applications.
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Need to evaluate generalization performance for various scenes and object types
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