# Out-of-Distribution Detection for Adaptive Computer Vision

Source: [Out-of-Distribution Detection for Adaptive Computer Vision](<https://www.sota2.com/research/paper/68fa6f5a1e209131cea45366>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:19:28.160Z

Authors: Simon Kristoffersson Lind, Rudolph Triebel, Luigi Nardi, Volker Krueger

Published: 2023-05-16T09:01:42.000Z

## Abstract

It is well known that computer vision can be unreliable when faced with previously unseen imaging conditions. This paper proposes a method to adapt camera parameters according to a normalizing flow-based out-of-distibution detector. A small-scale study is conducted which shows that adapting camera parameters according to this out-of-distibution detector leads to an average increase of 3 to 4 percentage points in mAP, mAR and F1 performance metrics of a YOLOv4 object detector. As a secondary result, this paper also shows that it is possible to train a normalizing flow model for out-of-distribution detection on the COCO dataset, which is larger and more diverse than most benchmarks for out-of-distibution detectors.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2305.09293>)

[PDF](<https://arxiv.org/pdf/2305.09293.pdf>)