Detecting Anomalies in Semantic Segmentation with Prototypes

TL;DR

This paper introduces a prototype learning approach for anomaly detection in semantic segmentation, enabling efficient identification of unknown objects with improved accuracy over existing generative model-based methods.

Contribution

It proposes a novel lightweight prototype-based method for anomaly segmentation that outperforms state-of-the-art generative models in accuracy and computational efficiency.

Findings

Achieves new state-of-the-art results on StreetHazards dataset.

Reduces computational overhead compared to generative models.

Effective detection of unknown objects in semantic segmentation.

Abstract

Traditional semantic segmentation methods can recognize at test time only the classes that are present in the training set. This is a significant limitation, especially for semantic segmentation algorithms mounted on intelligent autonomous systems, deployed in realistic settings. Regardless of how many classes the system has seen at training time, it is inevitable that unexpected, unknown objects will appear at test time. The failure in identifying such anomalies may lead to incorrect, even dangerous behaviors of the autonomous agent equipped with such segmentation model when deployed in the real world. Current state of the art of anomaly segmentation uses generative models, exploiting their incapability to reconstruct patterns unseen during training. However, training these models is expensive, and their generated artifacts may create false anomalies. In this paper we take a different route and we propose to address anomaly segmentation through prototype learning. Our intuition is that anomalous pixels are those that are dissimilar to all class prototypes known by the model. We extract class prototypes from the training data in a lightweight manner using a cosine similarity-based classifier. Experiments on StreetHazards show that our approach achieves the new state of the art, with a significant margin over previous works, despite the reduced computational overhead. Code is available at https://github.com/DarioFontanel/PAnS.