Source Free Object Detection
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Benchmarks
InBreast
Most implemented
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Context Aware Grounded Teacher for Source Free Object Detection
Dynamic Retraining-Updating Mean Teacher for Source-Free Object Detection
Simplifying Source-Free Domain Adaptation for Object Detection: Effective Self-Training Strategies and Performance Insights
CLIP-Guided Source-Free Object Detection in Aerial Images
Papers
Context Aware Grounded Teacher for Source Free Object Detection
We focus on the Source Free Object Detection (SFOD) problem, when source data is unavailable during adaptation, and the model must adapt to the unlabeled target domain. In medical imaging, several approaches have leverag…
object-detectionObject DetectionSource Free Object DetectionDynamic Retraining-Updating Mean Teacher for Source-Free Object Detection
In object detection, unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. However, UDA's reliance on labeled source data restricts its adaptability i…
Domain Adaptationobject-detectionObject DetectionSource-Free Domain Adaptation+2Enhancing Source-Free Domain Adaptive Object Detection with Low-confidence Pseudo Label Distillation
Source-Free domain adaptive Object Detection (SFOD) is a promising strategy for deploying trained detectors to new, unlabeled domains without accessing source data, addressing significant concerns around data privacy and…
object-detectionObject DetectionPseudo LabelRegion Proposal+1Simplifying Source-Free Domain Adaptation for Object Detection: Effective Self-Training Strategies and Performance Insights
This paper focuses on source-free domain adaptation for object detection in computer vision. This task is challenging and of great practical interest, due to the cost of obtaining annotated data sets for every new domain…
Domain Adaptationobject-detectionObject DetectionPseudo Label+2Multi-source-free Domain Adaptation via Uncertainty-aware Adaptive Distillation
Source-free domain adaptation (SFDA) alleviates the domain discrepancy among data obtained from domains without accessing the data for the awareness of data privacy. However, existing conventional SFDA methods face inher…
Domain AdaptationKnowledge DistillationSource-Free Domain AdaptationSource Free Object Detection+1Source-free Domain Adaptive Object Detection in Remote Sensing Images
Recent studies have used unsupervised domain adaptive object detection (UDAOD) methods to bridge the domain gap in remote sensing (RS) images. However, UDAOD methods typically assume that the source domain data can be ac…
Domain Adaptationobject-detectionObject DetectionSource Free Object Detection