# Detecting the Unexpected via Image Resynthesis

## TL;DR

This paper presents a novel approach for detecting unexpected objects in images by identifying poorly-resynthesized regions after semantic segmentation, outperforming existing uncertainty and autoencoder-based methods.

## Contribution

It introduces a new method that detects unknown objects by analyzing image resynthesis quality, diverging from traditional uncertainty or autoencoder-based techniques.

## Key findings

- Outperforms existing methods in unknown object detection
- Resynthesis-based detection effectively identifies unexpected objects
- Method demonstrates robustness across different scenarios

## Abstract

Classical semantic segmentation methods, including the recent deep learning ones, assume that all classes observed at test time have been seen during training. In this paper, we tackle the more realistic scenario where unexpected objects of unknown classes can appear at test time. The main trends in this area either leverage the notion of prediction uncertainty to flag the regions with low confidence as unknown, or rely on autoencoders and highlight poorly-decoded regions. Having observed that, in both cases, the detected regions typically do not correspond to unexpected objects, in this paper, we introduce a drastically different strategy: It relies on the intuition that the network will produce spurious labels in regions depicting unexpected objects. Therefore, resynthesizing the image from the resulting semantic map will yield significant appearance differences with respect to the input image. In other words, we translate the problem of detecting unknown classes to one of identifying poorly-resynthesized image regions. We show that this outperforms both uncertainty- and autoencoder-based methods.