Papers Source Free Object Detection
“Source Free Object Detection” 태그가 달린 논문 17편 · 필터 해제
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 DetectionCLIP-Guided Source-Free Object Detection in Aerial Images
Domain adaptation is crucial in aerial imagery, as the visual representation of these images can significantly vary based on factors such as geographic location, time, and weather conditions. Additionally, high-resolutio…
Domain AdaptationObjectobject-detectionObject Detection+4Periodically Exchange Teacher-Student for Source-Free Object Detection
Source-free object detection (SFOD) aims to adapt the source detector to unlabeled target domain data in the absence of source domain data. Most SFOD methods follow the same self-training paradigm using mean-teacher (MT)…
object-detectionObject DetectionSource Free Object DetectionExploiting Low-confidence Pseudo-labels for Source-free Object Detection
Source-free object detection (SFOD) aims to adapt a source-trained detector to an unlabeled target domain without access to the labeled source data. Current SFOD methods utilize a threshold-based pseudo-label approach in…
Contrastive Learningobject-detectionObject DetectionPseudo Label+1Adversarial Alignment for Source Free Object Detection
Source-free object detection (SFOD) aims to transfer a detector pre-trained on a label-rich source domain to an unlabeled target domain without seeing source data. While most existing SFOD methods generate pseudo labels …
Objectobject-detectionObject DetectionSource Free Object DetectionInstance Relation Graph Guided Source-Free Domain Adaptive Object Detection
Unsupervised Domain Adaptation (UDA) is an effective approach to tackle the issue of domain shift. Specifically, UDA methods try to align the source and target representations to improve the generalization on the target …
Domain AdaptationKnowledge DistillationObjectobject-detection+6Source-Free Object Detection by Learning To Overlook Domain Style
Source-free object detection (SFOD) needs to adapt a detector pre-trained on a labeled source domain to a target domain, with only unlabeled training data from the target domain. Existing SFOD methods typically adopt…
object-detectionObject DetectionSource Free Object DetectionModel Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data
Unsupervised domain adaptation aims to align a labeled source domain and an unlabeled target domain, but it requires to access the source data which often raises concerns in data privacy, data portability and data transm…
Contrastive LearningDomain AdaptationSource Free Object DetectionUnsupervised Domain AdaptationExploring Sequence Feature Alignment for Domain Adaptive Detection Transformers
Detection transformers have recently shown promising object detection results and attracted increasing attention. However, how to develop effective domain adaptation techniques to improve its cross-domain performance rem…
DecoderDomain AdaptationObjectobject-detection+3A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data
Unsupervised domain adaptation (UDA) assumes that source and target domain data are freely available and usually trained together to reduce the domain gap. However, considering the data privacy and the inefficiency of da…
Data AugmentationDomain AdaptationLearning with noisy labelsobject-detection+4Unbiased Mean Teacher for Cross-domain Object Detection
Cross-domain object detection is challenging, because object detection model is often vulnerable to data variance, especially to the considerable domain shift between two distinctive domains. In this paper, we propose a …
Objectobject-detectionObject DetectionSmall Data Image Classification+1Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
The recently proposed Temporal Ensembling has achieved state-of-the-art results in several semi-supervised learning benchmarks. It maintains an exponential moving average of label predictions on each training example, an…
Semi-Supervised Image ClassificationSemi-Supervised RGBD Semantic SegmentationSemi-Supervised Semantic SegmentationSource Free Object Detection