One of the most challenges for anomaly detection (AD) is
how to learn one unified and generalizable model to adapt
to multi-class especially cross-class settings: the model is
trained with normal samples from seen classes with the
objective to detect anomalies from both seen and unseen
classes. In this work, we propose a novel Proposal Masked
Anomaly Detection (termed as PMAD) approach for such
challenging multi- and cross-class anomaly detection. The
proposed PMAD can be adapted to seen and unseen classes
by two key designs: MAE-based patch-level reconstruction
and prototype-guided proposal masking. First, motivated by
MAE (Masked AutoEncoder), we develop a patch-level reconstruction
model rather than the image-level reconstruction
adopted in most AD methods for this reason: the masked
patches in unseen classes can be reconstructed well by using
the visible patches and the adaptive reconstruction capability
of MAE. Moreover, we improve MAE by ViT encoderdecoder
architecture, combinational masking, and visual tokens
as reconstruction objectives to make it more suitable for
anomaly detection. Second, we develop a two-stage anomaly
detection manner during inference. In the proposal masking
stage, the prototype-guided proposal masking module is utilized
to generate proposals for suspicious anomalies as much
as possible, then masked patches can be generated from the
proposal regions. By masking most likely anomalous patches,
the “shortcut reconstruction” issue (i.e., anomalous regions
can be well reconstructed) can be mostly avoided. In the
reconstruction stage, these masked patches are then reconstructed
by the trained patch-level reconstruction model to
determine if they are anomalies. Extensive experiments show
that the proposed PMAD can outperform current state-of-theart
models significantly under the multi- and especially crossclass
settings.

One-for-All: Proposal Masked Cross-Class Anomaly Detection

Posters & Demos I

poster

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Ruoqi Li

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One-for-All: Proposal Masked Cross-Class Anomaly Detection

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