# Detection-by-Localization: Maintenance-Free Change Object Detector

arXiv: https://arxiv.org/abs/1809.05267 · HTML: https://mlsanity.com/paper/1809.05267/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2018.

## Built on by (most cited first, 2 of 2)

- [Multi-Agent 3D Map Reconstruction and Change Detection in Microgravity with Free-Flying Robots](https://mlsanity.com/paper/2311.02558/) (2023)
- [Deep SIMBAD: Active Landmark-based Self-localization Using Ranking -based Scene Descriptor](https://mlsanity.com/paper/2109.02786/) (2021)

## Abstract (arXiv)

Recent researches demonstrate that self-localization performance is a very useful measure of likelihood-of-change (LoC) for change detection. In this paper, this "detection-by-localization" scheme is studied in a novel generalized task of object-level change detection. In our framework, a given query image is segmented into object-level subimages (termed "scene parts"), which are then converted to subimage-level pixel-wise LoC maps via the detection-by-localization scheme. Our approach models a self-localization system as a ranking function, outputting a ranked list of reference images, without requiring relevance score. Thanks to this new setting, we can generalize our approach to a broad class of self-localization systems. Our ranking based self-localization model allows to fuse self-localization results from different modalities via an unsupervised rank fusion derived from a field of multi-modal information retrieval (MMR).