ConfMix: Unsupervised Domain Adaptation for Object Detection via Confidence-based Mixing
This addresses the challenge of adapting object detectors to new domains without labeled data, which is an incremental improvement over existing methods.
The paper tackles the problem of unsupervised domain adaptation for object detection by proposing ConfMix, a method that uses confidence-based mixing and a novel pseudo labeling scheme, achieving state-of-the-art performance on two datasets and approaching supervised performance on another.
Unsupervised Domain Adaptation (UDA) for object detection aims to adapt a model trained on a source domain to detect instances from a new target domain for which annotations are not available. Different from traditional approaches, we propose ConfMix, the first method that introduces a sample mixing strategy based on region-level detection confidence for adaptive object detector learning. We mix the local region of the target sample that corresponds to the most confident pseudo detections with a source image, and apply an additional consistency loss term to gradually adapt towards the target data distribution. In order to robustly define a confidence score for a region, we exploit the confidence score per pseudo detection that accounts for both the detector-dependent confidence and the bounding box uncertainty. Moreover, we propose a novel pseudo labelling scheme that progressively filters the pseudo target detections using the confidence metric that varies from a loose to strict manner along the training. We perform extensive experiments with three datasets, achieving state-of-the-art performance in two of them and approaching the supervised target model performance in the other. Code is available at: https://github.com/giuliomattolin/ConfMix.