# Leveraging Confident Image Regions for Source-Free Domain-Adaptive Object Detection

Source: [Leveraging Confident Image Regions for Source-Free Domain-Adaptive Object Detection](<https://www.sota2.com/research/paper/68fa7be41e209131cea8b1ae>)

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

Data updated: 2025-10-26T05:51:17.805Z

Authors: Mohamed Lamine Mekhalfi, Davide Boscaini, Fabio Poiesi

Published: 2025-01-17T09:55:41.000Z

## Abstract

Source-free domain-adaptive object detection is an interesting but scarcely addressed topic. It aims at adapting a source-pretrained detector to a distinct target domain without resorting to source data during adaptation. So far, there is no data augmentation scheme tailored to source-free domain-adaptive object detection. To this end, this paper presents a novel data augmentation approach that cuts out target image regions where the detector is confident, augments them along with their respective pseudo-labels, and joins them into a challenging target image to adapt the detector. As the source data is out of reach during adaptation, we implement our approach within a teacher-student learning paradigm to ensure that the model does not collapse during the adaptation procedure. We evaluated our approach on three adaptation benchmarks of traffic scenes, scoring new state-of-the-art on two of them.

## 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/2501.10081>)

[PDF](<https://arxiv.org/pdf/2501.10081.pdf>)