Summary of View-invariant Pixelwise Anomaly Detection in Multi-object Scenes with Adaptive View Synthesis, by Subin Varghese et al.
View-Invariant Pixelwise Anomaly Detection in Multi-object Scenes with Adaptive View Synthesis
by Subin Varghese, Vedhus Hoskere
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the challenge of unsupervised anomaly detection in scenes with imperfectly aligned images. The Scene Anomaly Detection (Scene AD) problem arises when inspecting and monitoring infrastructure assets, as images from different instances in time may not be perfectly aligned. Current methods have been developed for industrial settings where camera positions are known and constant, but these fail to generalize to the case of imperfect alignment. To address this issue, the authors propose a novel network called OmniAD, which refines the anomaly detection method reverse distillation to achieve a 40% increase in pixel-level anomaly detection performance. The network’s performance is further improved with two new data augmentation strategies that leverage novel view synthesis and camera localization to improve generalization. The approach is validated on two datasets: ToyCity, a new Scene AD dataset with multiple objects, and MAD, an established single object-centric dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to find problems in pictures taken from different angles or at different times. It’s hard because the images might not match up perfectly. Right now, there are methods for finding problems in industrial settings where cameras are always in the same place, but these don’t work well when the camera position changes. This paper presents a new way to find unknown problems in scenes with imperfectly aligned images. The method uses a special type of AI called OmniAD that improves performance by 40%. It also introduces two new techniques to make the approach more robust. The results are demonstrated on two datasets, one for finding problems in complex scenes and another for finding issues with single objects. |
Keywords
» Artificial intelligence » Alignment » Anomaly detection » Data augmentation » Distillation » Generalization » Unsupervised
