# Semi-Supervised Object Detection

23 papers with code • 6 benchmarks • 1 datasets

This task has no description! Would you like to contribute one?

## Libraries

Use these libraries to find Semi-Supervised Object Detection models and implementations
4 papers
30
3 papers
11
2 papers
246

# A Simple Semi-Supervised Learning Framework for Object Detection

10 May 2020

Semi-supervised learning (SSL) has a potential to improve the predictive performance of machine learning models using unlabeled data.

4

# 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.

4

# 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.

3

# 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.

2

# Consistency-based Semi-supervised Learning for Object detection

Making a precise annotation in a large dataset is crucial to the performance of object detection.

1

# Temporal Self-Ensembling Teacher for Semi-Supervised Object Detection

13 Jul 2020

(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.

1

# 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$^*$.

1

# 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.

1

# MUM : Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection

22 Nov 2021

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.

1

# 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.

1