Di Huang, Guodong Wang, Jinjin Zhang, Yizhou Jin
We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| zhang0jhon/LogSAD | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| encode_text_with_prompt_ensemble | Ran | zhang0jhon/LogSAD/prompt_ensemble.py pointer only (licence: NONE) · get_code("d6cbed724d58041a") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Anomaly detection is valuable for real-world applications, such as industrial quality inspection. However, most approaches focus on detecting local structural anomalies while neglecting compositional anomalies incorporating logical constraints. In this paper, we introduce LogSAD, a novel multi-modal framework that requires no training for both Logical and Structural Anomaly Detection. First, we propose a match-of-thought architecture that employs advanced large multi-modal models (i.e. GPT-4V) to generate matching proposals, formulating interests and compositional rules of thought for anomaly detection. Second, we elaborate on multi-granularity anomaly detection, consisting of patch tokens, sets of interests, and composition matching with vision and language foundation models. Subsequently, we present a calibration module to align anomaly scores from different detectors, followed by integration strategies for the final decision. Consequently, our approach addresses both logical and structural anomaly detection within a unified framework and achieves state-of-the-art results without the need for training, even when compared to supervised approaches, highlighting its robustness and effectiveness. Code is available at https://github.com/zhang0jhon/LogSAD.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2503.18325")
get_code_for_paper("2503.18325")
have("2503.18325")
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