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
1Semi-DETRmAP40.1—Unverified
2MixPLmAP40.1—Unverified
3ARSLmAP34.45—Unverified
4Efficient TeachermAP34.11—Unverified
5MixTeacher-FRCNNmAP34.06—Unverified
6MixTeacher-FCOSmAP33.42—Unverified
7Dense TeachermAP33.01—Unverified
8PseComAP32.5—Unverified
9Revisiting Class ImbalancemAP32.21—Unverified
10VCmAP32.05—Unverified