SOTAVerified

Semi-Supervised Object Detection

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.

Papers

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Showing 1–10 of 115 papers

TitleStatusHype
Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection—0
ClipGrader: Leveraging Vision-Language Models for Robust Label Quality Assessment in Object Detection—0
Semi-Supervised Weed Detection in Vegetable Fields: In-domain and Cross-domain Experiments—0
SimLTD: Simple Supervised and Semi-Supervised Long-Tailed Object DetectionCode1
Co-Learning: Towards Semi-Supervised Object Detection with Road-side Cameras—0
Collaborative Feature-Logits Contrastive Learning for Open-Set Semi-Supervised Object Detection—0
Applying the Lower-Biased Teacher Model in Semi-Supervised Object Detection—0
Semi-Supervised 3D Object Detection with Channel Augmentation using Transformation Equivariance—0
Class-balanced Open-set Semi-supervised Object Detection for Medical Images—0
Semi-Supervised Object Detection: A Survey on Progress from CNN to Transformer—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MixPLmAP31.7—Unverified
2Consistent-TeachermAP25.5—Unverified
3ARSLmAP25.36—Unverified
4MixTeacher-FRCNNmAP25.16—Unverified
5VCmAP23.86—Unverified
6MixTeacher-FCOSmAP23.83—Unverified
7Efficient TeachermAP23.76—Unverified
8PseComAP22.43—Unverified
9MUMmAP21.88—Unverified
10SSOD with OCL and RUPLmAP21.63—Unverified