Semi-Supervised Object Detection

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

7 leaderboard tables shown for this task, 7 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 51 papers with code (115 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• End-to-End Semi-Supervised Object Detection with Soft Teacher
  16 Jun 2021 8 repositories listed
  This paper presents an end-to-end semi-supervised object detection approach, in contrast to previous more complex multi-stage methods.
• A Simple Semi-Supervised Learning Framework for Object Detection
  10 May 2020 7 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  Semi-supervised learning (SSL) has a potential to improve the predictive performance of machine learning models using unlabeled data.
• Efficient Teacher: Semi-Supervised Object Detection for YOLOv5
  15 Feb 2023 4 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  The Pseudo Label Assigner prevents the occurrence of bias caused by a large number of low-quality pseudo labels that may interfere with the Dense Detector during the student-teacher mutual learning mechanism, and the…
• Unbiased Teacher for Semi-Supervised Object Detection
  18 Feb 2021 4 repositories listed
  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.
• Semi-DETR: Semi-Supervised Object Detection with Detection Transformers
  16 Jul 2023 3 repositories listed
  Specifically, we propose a Stage-wise Hybrid Matching strategy that combines the one-to-many assignment and one-to-one assignment strategies to improve the training efficiency of the first stage and thus provide…
• Label Matching Semi-Supervised Object Detection
  14 Jun 2022 3 repositories listed
  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…
• Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection
  6 Jul 2022 2 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  To date, the most powerful semi-supervised object detectors (SS-OD) are based on pseudo-boxes, which need a sequence of post-processing with fine-tuned hyper-parameters.
• Consistency-based Semi-supervised Learning for Object detection
  1 Dec 2019 2 repositories listed
  Making a precise annotation in a large dataset is crucial to the performance of object detection.
• SimLTD: Simple Supervised and Semi-Supervised Long-Tailed Object Detection
  28 Dec 2024 1 repository listed
  Recent years have witnessed tremendous advances on modern visual recognition systems.
• Multi-clue Consistency Learning to Bridge Gaps Between General and Oriented Object in Semi-supervised Detection
  8 Jul 2024 1 repository listed
  We experimentally find three gaps between general and oriented object detection in semi-supervised learning: 1) Sampling inconsistency: the common center sampling is not suitable for oriented objects with larger aspect…
• SOOD++: Leveraging Unlabeled Data to Boost Oriented Object Detection
  1 Jul 2024 1 repository listed
  Specifically, we observe that objects from aerial images are usually arbitrary orientations, small scales, and aggregation, which inspires the following core designs: a Simple Instance-aware Dense Sampling (SIDS)…
• Power of Cooperative Supervision: Multiple Teachers Framework for Enhanced 3D Semi-Supervised Object Detection
  31 May 2024 1 repository listed
  To address these two issues, we have constructed a multi-class 3D LiDAR dataset reflecting diverse urban environments and object characteristics, and developed a robust 3D semi-supervised object detection (SSOD) based…
• Gradient-based Sampling for Class Imbalanced Semi-supervised Object Detection
  22 Mar 2024 1 repository listed
  To tackle the confirmation bias from incorrect pseudo labels of minority classes, the class-rebalancing sampling module resamples unlabeled data following the guidance of the gradient-based reweighting module.
• Mixed Pseudo Labels for Semi-Supervised Object Detection
  12 Dec 2023 1 repository listed
  While the pseudo-label method has demonstrated considerable success in semi-supervised object detection tasks, this paper uncovers notable limitations within this approach.
• Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object Detection
  5 Dec 2023 1 repository listed Syntology ran 5 of 8 samples · 3 unverified · 8 pointer-only (licence)
  Semi-supervised object detection is crucial for 3D scene understanding, efficiently addressing the limitation of acquiring large-scale 3D bounding box annotations.
• LEOD: Label-Efficient Object Detection for Event Cameras
  29 Nov 2023 1 repository listed Syntology ran 4 of 4 samples · 0 unverified
  On 1Mpx, RVT-S with 10% labels even surpasses its fully-supervised counterpart using 100% labels.
• Density-Guided Dense Pseudo Label Selection For Semi-supervised Oriented Object Detection
  21 Nov 2023 1 repository listed
  Therefore, we propose Density-Guided Dense Pseudo Label Selection (DDPLS) for semi-supervised oriented object detection.
• Towards Few-Annotation Learning for Object Detection: Are Transformer-based Models More Efficient ?
  30 Oct 2023 1 repository listed
  For specialized and dense downstream tasks such as object detection, labeling data requires expertise and can be very expensive, making few-shot and semi-supervised models much more attractive alternatives.
• Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object Detection
  26 Oct 2023 1 repository listed Syntology ran 2 of 2 samples · 0 unverified
  Federated Learning (FL) has emerged as a potent framework for training models across distributed data sources while maintaining data privacy.
• Semi-Supervised Object Detection with Uncurated Unlabeled Data for Remote Sensing Images
  9 Oct 2023 1 repository listed
  Semi-supervised object detection (SSOD) methods tackle this issue by generating pseudo-labels for the unlabeled data, assuming that all classes found in the unlabeled dataset are also represented in the labeled data.
• ALWOD: Active Learning for Weakly-Supervised Object Detection
  14 Sep 2023 1 repository listed
  In this work, we propose ALWOD, a new framework that addresses this problem by fusing active learning (AL) with weakly and semi-supervised object detection paradigms.
• Density Crop-guided Semi-supervised Object Detection in Aerial Images
  9 Aug 2023 1 repository listed
  One of the important bottlenecks in training modern object detectors is the need for labeled images where bounding box annotations have to be produced for each object present in the image.
• SOOD: Towards Semi-Supervised Oriented Object Detection
  10 Apr 2023 1 repository listed
  Semi-Supervised Object Detection (SSOD), aiming to explore unlabeled data for boosting object detectors, has become an active task in recent years.
• Hierarchical Supervision and Shuffle Data Augmentation for 3D Semi-Supervised Object Detection
  4 Apr 2023 1 repository listed Syntology ran 0 of 6 samples · 6 unverified
  State-of-the-art 3D object detectors are usually trained on large-scale datasets with high-quality 3D annotations.
• Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection
  27 Mar 2023 1 repository listed
  It employs a "divide-and-conquer" strategy and separately exploits positives for the classification and localization task, which is more robust to the assignment ambiguity.
• MixTeacher: Mining Promising Labels with Mixed Scale Teacher for Semi-Supervised Object Detection
  16 Mar 2023 1 repository listed
  Despite the remarkable progress made by modern detection models, this challenge is particularly evident in the semi-supervised case.
• Active Teacher for Semi-Supervised Object Detection
  15 Mar 2023 1 repository listed
  In this paper, we study teacher-student learning from the perspective of data initialization and propose a novel algorithm called Active Teacher(Source code are available at: \url{https://github.
• Towards End-to-end Semi-supervised Learning for One-stage Object Detection
  22 Feb 2023 1 repository listed
  In addition to this challenge, we also reveal two key issues in one-stage SSOD, which are low-quality pseudo-labeling and multi-task optimization conflict, respectively.
• Learning Object-level Point Augmentor for Semi-supervised 3D Object Detection
  19 Dec 2022 1 repository listed
  In this work, we propose an object-level point augmentor (OPA) that performs local transformations for semi-supervised 3D object detection.
• Adaptive Self-Training for Object Detection
  7 Dec 2022 1 repository listed
  In this work, we introduce our method Adaptive Self-Training for Object Detection (ASTOD), which is a simple yet effective teacher-student method.

Syntology lines on 7 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.