Semi-Supervised Object Detection
45 papers with code • 6 benchmarks • 1 datasets
Semi-supervised object detection uses both labeled data and unlabeled data for training. It not only reduces the annotation burden for training high-performance object detectors but also further improves the object detector by using a large number of unlabeled data.
Benchmarks
These leaderboards are used to track progress in Semi-Supervised Object Detection
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Libraries
Use these libraries to find Semi-Supervised Object Detection models and implementations
COCO 10% labeled data
Most implemented papers
Points as Queries: Weakly Semi-supervised Object Detection by Points
haozheliu-st/point-beyond-class
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CVPR 2021
We propose a novel point annotated setting for the weakly semi-supervised object detection task, in which the dataset comprises small fully annotated images and large weakly annotated images by points.
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
jongmokkim/mix-unmix
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CVPR 2022
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.
CrossRectify: Leveraging Disagreement for Semi-supervised Object Detection
machengcheng2016/crossteaching-ssod
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Semi-supervised object detection has recently achieved substantial progress.
DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object Detection
divadi/detmatch
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While numerous 3D detection works leverage the complementary relationship between RGB images and point clouds, developments in the broader framework of semi-supervised object recognition remain uninfluenced by multi-modal fusion.
SIOD: Single Instance Annotated Per Category Per Image for Object Detection
Object detection under imperfect data receives great attention recently.
PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised Object Detection
ligang-cs/PseCo
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Specifically, for pseudo labeling, existing works only focus on the classification score yet fail to guarantee the localization precision of pseudo boxes; For consistency training, the widely adopted random-resize training only considers the label-level consistency but misses the feature-level one, which also plays an important role in ensuring the scale invariance.
Omni-DETR: Omni-Supervised Object Detection with Transformers
amazon-research/omni-detr
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CVPR 2022
This is enabled by a unified architecture, Omni-DETR, based on the recent progress on student-teacher framework and end-to-end transformer based object detection.
Dense Learning based Semi-Supervised Object Detection
chenbinghui1/dsl
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CVPR 2022
Semi-supervised object detection (SSOD) aims to facilitate the training and deployment of object detectors with the help of a large amount of unlabeled data.
