Semi-Supervised Object Detection
23 papers with code • 6 benchmarks • 1 datasets
Libraries
Use these libraries to find Semi-Supervised Object Detection models and implementationsMost implemented papers
A Simple Semi-Supervised Learning Framework for Object Detection
Semi-supervised learning (SSL) has a potential to improve the predictive performance of machine learning models using unlabeled data.
End-to-End Semi-Supervised Object Detection with Soft Teacher
This paper presents an end-to-end semi-supervised object detection approach, in contrast to previous more complex multi-stage methods.
Unbiased Teacher for Semi-Supervised Object Detection
To address this, we introduce Unbiased Teacher, a simple yet effective approach that jointly trains a student and a gradually progressing teacher in a mutually-beneficial manner.
Label Matching Semi-Supervised Object Detection
To remedy this issue, we present a novel label assignment mechanism for self-training framework, namely proposal self-assignment, which injects the proposals from student into teacher and generates accurate pseudo labels to match each proposal in the student model accordingly.
Consistency-based Semi-supervised Learning for Object detection
Making a precise annotation in a large dataset is crucial to the performance of object detection.
Temporal Self-Ensembling Teacher for Semi-Supervised Object Detection
(1) The teacher model serves a dual role as a teacher and a student, such that the teacher predictions on unlabeled images may be very close to those of student, which limits the upper-bound of the student.
Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework
To alleviate the confirmation bias problem and improve the quality of pseudo annotations, we further propose a co-rectify scheme based on Instant-Teaching, denoted as Instant-Teaching$^*$.
DETReg: Unsupervised Pretraining with Region Priors for Object Detection
Recent self-supervised pretraining methods for object detection largely focus on pretraining the backbone of the object detector, neglecting key parts of detection architecture.
MUM : Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection
Data augmentation strategy plays a significant role in the SSL framework since it is hard to create a weak-strong augmented input pair without losing label information.
MUM: Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection
Data augmentation strategy plays a significant role in the SSL framework since it is hard to create a weak-strong augmented input pair without losing label information.