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

• URL: http://arxiv.org/abs/2501.10081v1
• Date: Fri, 17 Jan 2025 09:55:41 GMT
• Title: Leveraging Confident Image Regions for Source-Free Domain-Adaptive Object Detection
• Authors: Mohamed Lamine Mekhalfi, Davide Boscaini, Fabio Poiesi,
• Abstract summary: 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. 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.
• Score: 6.627477206883247
• License:
• 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.

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