Summary of Flowclas: Enhancing Normalizing Flow Via Contrastive Learning For Anomaly Segmentation, by Chang Won Lee et al.
FlowCLAS: Enhancing Normalizing Flow Via Contrastive Learning For Anomaly Segmentation
by Chang Won Lee, Selina Leveugle, Svetlana Stolpner, Chris Langley, Paul Grouchy, Jonathan Kelly, Steven L. Waslander
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 The proposed FlowCLAS framework is a self-supervised approach to anomaly segmentation that leverages vision foundation models and normalizing flow networks. It extracts rich features from images and learns their density distribution, which enhances its discriminative power through Outlier Exposure and contrastive learning in the latent space. This novel framework significantly outperforms existing methods on the ALLO benchmark for space robotics and demonstrates competitive results on road anomaly segmentation benchmarks for autonomous driving, including Fishyscapes Lost&Found and Road Anomaly. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Anomaly segmentation is important for safety-critical applications that need to detect unexpected events. Current methods rely on labeled data, which limits their ability to use unlabeled data and pre-trained models. The new FlowCLAS method is different because it learns from unlabeled images and doesn’t require labels. This makes it better at detecting anomalies in scenes with limited colors and objects. |
Keywords
» Artificial intelligence » Latent space » Self supervised
